Revert python version change and update actions/setup-python to v5

* Fix batch count

* Add button to unload models manually.

* Add compiled pipeline option

* Add brevitas to requirements

* Tweaks to chatbot

* Change script loading trigger

.flake8

@@ -0,0 +1,5 @@
+[flake8]
+count = 1
+show-source = 1
+select = E9,F63,F7,F82
+exclude = lit.cfg.py, apps/language_models/scripts/vicuna.py, apps/language_models/src/pipelines/minigpt4_pipeline.py, apps/language_models/langchain/h2oai_pipeline.py

.github/workflows/nightly.yml

@@ -14,12 +14,12 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
 
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
@@ -44,31 +44,20 @@ jobs:
         body: |
           Automatic snapshot release of nod.ai SHARK.
         draft: true
-        prerelease: false
+        prerelease: true
 
-    - name: Build Package 
+    - name: Build Package (api only)
       shell: powershell
       run: |
         ./setup_venv.ps1
-        pyinstaller .\apps\stable_diffusion\shark_sd.spec
-        mv ./dist/shark_sd.exe ./dist/shark_sd_${{ env.package_version_ }}.exe
-        signtool sign /f C:\shark_2023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_${{ env.package_version_ }}.exe
-        pyinstaller .\apps\stable_diffusion\shark_sd_cli.spec
-        mv ./dist/shark_sd_cli.exe ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
-        signtool sign /f C:\shark_2023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
+        python process_skipfiles.py
+        $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
+        pip install -e .
+        pip freeze -l
+        pyinstaller .\apps\shark_studio\shark_studio_apionly.spec
+        mv ./dist/nodai_shark_studio.exe ./dist/nodai_shark_studio_${{ env.package_version_ }}.exe
+        signtool sign /f c:\g\shark_02152023.cer /fd certHash /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/nodai_shark_studio_${{ 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
@@ -76,7 +65,8 @@ jobs:
         GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
       with:
         release_id: ${{ steps.create_release.outputs.id }}
-        assets_path: ./dist/*
+        assets_path: ./dist/nodai*
+        #asset_content_type: application/vnd.microsoft.portable-executable 
 
     - name: Publish Release
       id: publish_release
@@ -85,80 +75,3 @@ jobs:
         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"]
-        backend: [IREE, SHARK]
-
-    steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
-      with:
-        python-version: ${{ matrix.python-version }}
-    
-    - name: Setup pip cache
-      uses: actions/cache@v3
-      with:
-        path: ~/.cache/pip
-        key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
-        restore-keys: |
-          ${{ runner.os }}-pip-
-
-    - name: Install dependencies
-      run: |
-        echo "DATE=$(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 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
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude shark.venv,lit.cfg.py 
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        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 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/" -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 $GITHUB_WORKSPACE/gen_shark_tank/* gs://shark_tank/${DATE}_$SHA
-            gsutil -m cp -r gs://shark_tank/${DATE}_$SHA/* gs://shark_tank/nightly/
-        fi
-        rm -rf ./wheelhouse/nodai*
-
-    - name: Build and validate the SHARK Runtime package
-      if: ${{ matrix.backend == 'SHARK' }}
-      run: |
-        cd $GITHUB_WORKSPACE
-        ./setup_venv.sh
-        source shark.venv/bin/activate
-        package_version="$(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 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} -k "not metal" |
-          tail -n 1 |
-          tee -a pytest_results.txt

.github/workflows/test-models.yml

@@ -1,162 +0,0 @@
-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
-# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
-
-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: [7950x, icelake, a100, MacStudio, ubuntu-latest]
-        suite: [cpu,cuda,vulkan]
-        python-version: ["3.10"]
-        include:
-          - os: ubuntu-latest
-            suite: lint
-        exclude:
-          - os: ubuntu-latest
-            suite: vulkan
-          - os: ubuntu-latest
-            suite: cuda
-          - os: ubuntu-latest
-            suite: cpu
-          - os: MacStudio
-            suite: cuda
-          - os: MacStudio
-            suite: cpu
-          - 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
-        
-    - name: Set up Python Version File ${{ matrix.python-version }}
-      if: matrix.os == 'a100' ||  matrix.os == 'ubuntu-latest' ||  matrix.os == 'icelake'
-      run: |
-        # See https://github.com/actions/setup-python/issues/433
-        echo ${{ matrix.python-version }} >> $GITHUB_WORKSPACE/.python-version
-    
-    - name: Set up Python ${{ matrix.python-version }}
-      if: matrix.os == 'a100' ||  matrix.os == 'ubuntu-latest' ||  matrix.os == 'icelake'
-      uses: actions/setup-python@v4
-      with:
-        python-version: '${{ matrix.python-version }}'
-        #cache: 'pip'
-        #cache-dependency-path: |
-        #  **/requirements-importer.txt
-        #  **/requirements.txt
-    
-    - uses: actions/checkout@v2
-      if: matrix.os == '7950x'
-          
-    - name: Install dependencies
-      if: matrix.suite == 'lint'
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install flake8 pytest toml black
-        
-    - name: Lint with flake8
-      if: matrix.suite == 'lint'
-      run: |
-        # black format check
-        black --version
-        black --line-length 79 --check .
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude lit.cfg.py
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude lit.cfg.py
-
-    - name: Validate Models on CPU
-      if: matrix.suite == 'cpu'
-      run: |
-        cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
-        source shark.venv/bin/activate
-        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
-
-    - name: Validate Models on NVIDIA GPU
-      if: matrix.suite == 'cuda'
-      run: |
-        cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
-        source shark.venv/bin/activate
-        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 (MacOS)
-      if: matrix.suite == 'vulkan' && matrix.os == 'MacStudio'
-      run: |
-        cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
-        source shark.venv/bin/activate
-        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

.github/workflows/test-studio.yml

@@ -0,0 +1,85 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Validate Shark Studio
+
+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: [nodai-ubuntu-builder-large]
+        suite: [cpu] #,cuda,vulkan]
+        python-version: ["3.11"]
+        include:
+          - os: nodai-ubuntu-builder-large
+            suite: lint
+
+    runs-on: ${{ matrix.os }}
+
+    steps:
+    - uses: actions/checkout@v3
+    
+    - 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: Set up Python Version File ${{ matrix.python-version }}
+      run: |
+        echo ${{ matrix.python-version }} >> $GITHUB_WORKSPACE/.python-version
+    
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: '${{ matrix.python-version }}'
+          
+    - name: Install dependencies
+      if: matrix.suite == 'lint'
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest toml black
+        
+    - name: Lint with flake8
+      if: matrix.suite == 'lint'
+      run: |
+        # black format check
+        black --version
+        black --check apps/shark_studio 
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --isolated --count --exit-zero --max-complexity=10 --max-line-length=127 \
+          --statistics --exclude lit.cfg.py
+
+    - name: Validate Models on CPU
+      if: matrix.suite == 'cpu'
+      run: |
+        cd $GITHUB_WORKSPACE
+        python${{ matrix.python-version }} -m venv shark.venv
+        source shark.venv/bin/activate
+        pip install -r requirements.txt --no-cache-dir
+        pip install -e .
+        # Disabled due to hang when exporting test llama2
+        # python apps/shark_studio/tests/api_test.py

.gitignore

@@ -2,6 +2,8 @@
 __pycache__/
 *.py[cod]
 *$py.class
+*.mlir
+*.vmfb
 
 # C extensions
 *.so
@@ -157,17 +159,20 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
 # vscode related
 .vscode
 
-# Shark related artefacts
+# Shark related artifacts
 *venv/
 shark_tmp/
 *.vmfb
 .use-iree
 tank/dict_configs.py
+*.csv
+reproducers/
+apps/shark_studio/web/configs
 
 # ORT related artefacts
 cache_models/
@@ -178,7 +183,29 @@ generated_imgs/
 
 # Custom model related artefacts
 variants.json
-models/
+/models/
+*.safetensors
 
 # models folder
 apps/stable_diffusion/web/models/
+
+# model artifacts (SHARK)
+*.tempfile
+*.mlir
+*.vmfb
+
+# Stencil annotators.
+stencil_annotator/
+
+# For DocuChat
+apps/language_models/langchain/user_path/
+db_dir_UserData
+
+# Embeded browser cache and other
+apps/stable_diffusion/web/EBWebView/
+
+# Llama2 tokenizer configs
+llama2_tokenizer_configs/
+
+# Webview2 runtime artefacts
+EBWebView/

.gitmodules

@@ -1,4 +1,4 @@
 [submodule "inference/thirdparty/shark-runtime"]
 	path = inference/thirdparty/shark-runtime
-	url =https://github.com/nod-ai/SHARK-Runtime.git
+	url =https://github.com/nod-ai/SRT.git
 	branch = shark-06032022

.style.yapf

@@ -1,3 +0,0 @@
-[style]
-  based_on_style = google
-  column_limit = 80

README.md

@@ -2,18 +2,20 @@
 
 High Performance Machine Learning Distribution
 
+*We are currently rebuilding SHARK to take advantage of [Turbine](https://github.com/nod-ai/SHARK-Turbine). Until that is complete make sure you use an .exe release or a checkout of the `SHARK-1.0` branch, for a working SHARK*
+
 [![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)
 
 
 <details>
   <summary>Prerequisites - Drivers </summary>
-  
+
 #### Install your Windows hardware drivers
-* [AMD RDNA Users] Download this specific driver [here](https://www.amd.com/en/support/kb/release-notes/rn-rad-win-22-11-1-mril-iree). Latest drivers may not work.
-* [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. 
+* [AMD RDNA Users] Download the latest driver (23.2.1 is the oldest supported) [here](https://www.amd.com/en/support).
+* [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.
 
@@ -22,27 +24,46 @@ Other users please ensure you have your latest vendor drivers and Vulkan SDK fro
 </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 
+Install the 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. 
+Download the [stable release](https://github.com/nod-ai/shark/releases/latest) or the most recent [SHARK 1.0 pre-release](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. 
+Double click the .exe, or [run from the command line](#running) (recommended), and you should have the [UI](http://localhost:8080/) in the browser.
 
-If you have custom models (ckpt, safetensors) put in a `models/` directory where the .exe is. 
+If you have custom models put them in a `models/` directory where the .exe is.
 
-Enjoy. 
+Enjoy.
 
-Some known AMD Driver quirks and fixes with cursors are documented [here](https://github.com/nod-ai/SHARK/blob/main/apps/stable_diffusion/stable_diffusion_amd.md ).
+<details>
+  <summary>More installation notes</summary>
+* We recommend that you download 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 with `rm *.vmfb`. You can also 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`
 
+## 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)
+* The first run may take few minutes when the models are downloaded and compiled. Your patience is appreciated. The download could be about 5GB.
+* 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/.
+* If you prefer to always run in the browser, use the `--ui=web` command argument when running the EXE.
+
+## Stopping
+
+* Select the command prompt that's running the EXE. Press CTRL-C and wait a moment or close the terminal.
+</details>
 
 <details>
   <summary>Advanced Installation (Only for developers)</summary>
-  
+
 ## Advanced Installation (Windows, Linux and macOS) for developers
 
+### Windows 10/11 Users
+
+* Install Git for Windows from [here](https://git-scm.com/download/win) if you don't already have it.
+
 ## Check out the code
 
 ```shell
@@ -50,13 +71,21 @@ git clone https://github.com/nod-ai/SHARK.git
 cd SHARK
 ```
 
+## Switch to the Correct Branch (IMPORTANT!)
+
+Currently SHARK is being rebuilt for [Turbine](https://github.com/nod-ai/SHARK-Turbine) on the `main` branch. For now you are strongly discouraged from using `main` unless you are working on the rebuild effort, and should not expect the code there to produce a working application for Image Generation, So for now you'll need switch over to the `SHARK-1.0` branch and use the stable code.
+
+```shell
+git checkout SHARK-1.0
+```
+
+The following setup instructions assume you are on this branch.
+
 ## Setup your Python VirtualEnvironment and Dependencies
 
 ### Windows 10/11 Users
 
-* Install the latest Python 3.10.x version from [here](https://www.python.org/downloads/windows/)
-
-* Install Git for Windows from [here](https://git-scm.com/download/win)
+* Install the latest Python 3.11.x version from [here](https://www.python.org/downloads/windows/)
 
 #### Allow the install script to run in Powershell
 ```powershell
@@ -72,21 +101,20 @@ set-executionpolicy remotesigned
 
 ```shell
 ./setup_venv.sh
-source shark.venv/bin/activate
+source shark1.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
+(shark1.venv) PS C:\g\shark> cd .\apps\stable_diffusion\web\
+(shark1.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
+(shark1.venv) > cd apps/stable_diffusion/web
+(shark1.venv) > python index.py
 ```
 
 #### Access Stable Diffusion on http://localhost:8080/?__theme=dark
@@ -100,21 +128,20 @@ source shark.venv/bin/activate
 
 #### 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"
+(shark1.venv) PS C:\g\shark> python .\apps\stable_diffusion\scripts\main.py --app="txt2img" --precision="fp16" --prompt="tajmahal, snow, sunflowers, oil on canvas" --device="vulkan"
 ```
 
 #### Linux / macOS Users
 ```shell
-python3.10 apps/stable_diffusion/scripts/txt2img.py --precision=fp16 --device=vulkan --prompt="tajmahal, oil on canvas, sunflowers, 4k, uhd"
+python3.11 apps/stable_diffusion/scripts/main.py --app=txt2img --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 7900XTX would like:
+The output on a AMD 7900XTX would look something like:
 
-```shell 
-Stats for run 0:
+```shell
 Average step time: 47.19188690185547ms/it
 Clip Inference time (ms) = 109.531
 VAE Inference time (ms): 78.590
@@ -129,7 +156,7 @@ Here are some samples generated:
 ![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. 
+Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any trouble with running it on your hardware.
 
 
 <details>
@@ -140,7 +167,7 @@ Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any
 This step sets up a new VirtualEnv for Python
 
 ```shell
-python --version #Check you have 3.10 on Linux, macOS or Windows Powershell
+python --version #Check you have 3.11 on Linux, macOS or Windows Powershell
 python -m venv shark_venv
 source shark_venv/bin/activate   # Use shark_venv/Scripts/activate on Windows
 
@@ -154,10 +181,10 @@ 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://nod-ai.github.io/SHARK-Runtime/pip-release-links.html --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/SRT/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
 ```
 
 ### Run shark tank model tests.
@@ -189,10 +216,10 @@ python ./minilm_jit.py --device="cpu"  #use cuda or vulkan or metal
 <details>
   <summary>Development, Testing and Benchmarks</summary>
 
-If you want to use Python3.10 and with TF Import tools you can use the environment variables like:
+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=python3.10 VENV_DIR=0617_venv IMPORTER=1 ./setup_venv.sh 
+# 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
@@ -201,15 +228,15 @@ python -m  shark.examples.shark_inference.resnet50_script --device="cpu" # Use g
 # Or a pytest
 pytest tank/test_models.py -k "MiniLM"
 ```
-  
 
+### How to use your locally built IREE / Torch-MLIR with SHARK
 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://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.
 
-### How to use your locally built Torch-MLIR with SHARK
+How to use your locally built Torch-MLIR with SHARK:
 ```shell
 1.) Run `./setup_venv.sh in SHARK` and activate `shark.venv` virtual env.
 2.) Run `pip uninstall torch-mlir`.
@@ -227,15 +254,20 @@ Now the SHARK will use your locally build Torch-MLIR repo.
 
 ## Benchmarking Dispatches
 
-To produce benchmarks of individual dispatches, you can add `--dispatch_benchmarks=All --dispatch_benchmarks_dir=<output_dir>` to your command line argument.  
+To produce benchmarks of individual dispatches, you can add `--dispatch_benchmarks=All --dispatch_benchmarks_dir=<output_dir>` to your pytest 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"`
 
+For example, to generate and run dispatch benchmarks for MiniLM on CUDA:
+```
+pytest -k "MiniLM and torch and static and cuda" --benchmark_dispatches=All -s --dispatch_benchmarks_dir=./my_dispatch_benchmarks
+```
+The given command will populate `<dispatch_benchmarks_dir>/<model_name>/` with an `ordered_dispatches.txt` that lists and orders the dispatches and their latencies, as well as folders for each dispatch that contain .mlir, .vmfb, and results of the benchmark for that dispatch.
+
 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",
@@ -246,14 +278,14 @@ shark_module = SharkInference(
 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 
+- 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.
+See tank/README.md for further instructions on how to run model tests and benchmarks from the SHARK tank.
 
 </details>
 
@@ -278,7 +310,7 @@ torch_mlir, func_name = mlir_importer.import_mlir(tracing_required=True)
 # SharkInference accepts mlir in linalg, mhlo, and tosa dialect.
 
 from shark.shark_inference import SharkInference
-shark_module = SharkInference(torch_mlir, func_name, device="cpu", mlir_dialect="linalg")
+shark_module = SharkInference(torch_mlir, device="cpu", mlir_dialect="linalg")
 shark_module.compile()
 result = shark_module.forward((input))
 
@@ -301,15 +333,20 @@ mhlo_ir = r"""builtin.module  {
 
 arg0 = np.ones((1, 4)).astype(np.float32)
 arg1 = np.ones((4, 1)).astype(np.float32)
-shark_module = SharkInference(mhlo_ir, func_name="forward", device="cpu", mlir_dialect="mhlo")
+shark_module = SharkInference(mhlo_ir, device="cpu", mlir_dialect="mhlo")
 shark_module.compile()
 result = shark_module.forward((arg0, arg1))
 ```
 </details>
 
+## Examples Using the REST API
+
+* [Setting up SHARK for use with Blender](./docs/shark_sd_blender.md)
+* [Setting up SHARK for use with Koboldcpp](./docs/shark_sd_koboldcpp.md)
+
 ## Supported and Validated Models
 
-SHARK is maintained to support the latest innovations in ML Models: 
+SHARK is maintained to support the latest innovations in ML Models:
 
 | TF HuggingFace Models | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
 |---------------------|----------|----------|-------------|
@@ -335,7 +372,7 @@ For a complete list of the models supported in SHARK, please refer to [tank/READ
 
 *   [Upstream IREE issues](https://github.com/google/iree/issues): Feature requests,
     bugs, and other work tracking
-*   [Upstream IREE Discord server](https://discord.gg/26P4xW4): Daily development
+*   [Upstream IREE Discord server](https://discord.gg/wEWh6Z9nMU): Daily development
     discussions with the core team and collaborators
 *   [iree-discuss email list](https://groups.google.com/forum/#!forum/iree-discuss):
     Announcements, general and low-priority discussion
@@ -350,7 +387,7 @@ For a complete list of the models supported in SHARK, please refer to [tank/READ
 *  Weekly meetings on Mondays 9AM PST. See [here](https://discourse.llvm.org/t/community-meeting-developer-hour-refactoring-recurring-meetings/62575) for more information.
 * [MLIR topic within LLVM Discourse](https://llvm.discourse.group/c/llvm-project/mlir/31) SHARK and IREE is enabled by and heavily relies on [MLIR](https://mlir.llvm.org).
 </details>
-  
+
 ## License
 
 nod.ai SHARK is licensed under the terms of the Apache 2.0 License with LLVM Exceptions.

apps/shark_studio/api/controlnet.py

@@ -0,0 +1,107 @@
+# from turbine_models.custom_models.controlnet import control_adapter, preprocessors
+import os
+import PIL
+import numpy as np
+from apps.shark_studio.web.utils.file_utils import (
+    get_generated_imgs_path,
+)
+from datetime import datetime
+from PIL import Image
+from gradio.components.image_editor import (
+    EditorValue,
+)
+
+
+class control_adapter:
+    def __init__(
+        self,
+        model: str,
+    ):
+        self.model = None
+
+    def export_control_adapter_model(model_keyword):
+        return None
+
+    def export_xl_control_adapter_model(model_keyword):
+        return None
+
+
+class preprocessors:
+    def __init__(
+        self,
+        model: str,
+    ):
+        self.model = None
+
+    def export_controlnet_model(model_keyword):
+        return None
+
+
+control_adapter_map = {
+    "sd15": {
+        "canny": {"initializer": control_adapter.export_control_adapter_model},
+        "openpose": {"initializer": control_adapter.export_control_adapter_model},
+        "scribble": {"initializer": control_adapter.export_control_adapter_model},
+        "zoedepth": {"initializer": control_adapter.export_control_adapter_model},
+    },
+    "sdxl": {
+        "canny": {"initializer": control_adapter.export_xl_control_adapter_model},
+    },
+}
+preprocessor_model_map = {
+    "canny": {"initializer": preprocessors.export_controlnet_model},
+    "openpose": {"initializer": preprocessors.export_controlnet_model},
+    "scribble": {"initializer": preprocessors.export_controlnet_model},
+    "zoedepth": {"initializer": preprocessors.export_controlnet_model},
+}
+
+
+class PreprocessorModel:
+    def __init__(
+        self,
+        hf_model_id,
+        device="cpu",
+    ):
+        self.model = hf_model_id
+        self.device = device
+
+    def compile(self):
+        print("compile not implemented for preprocessor.")
+        return
+
+    def run(self, inputs):
+        print("run not implemented for preprocessor.")
+        return inputs
+
+
+def cnet_preview(model, input_image):
+    curr_datetime = datetime.now().strftime("%Y-%m-%d.%H-%M-%S")
+    control_imgs_path = os.path.join(get_generated_imgs_path(), "control_hints")
+    if not os.path.exists(control_imgs_path):
+        os.mkdir(control_imgs_path)
+    img_dest = os.path.join(control_imgs_path, model + curr_datetime + ".png")
+    match model:
+        case "canny":
+            canny = PreprocessorModel("canny")
+            result = canny(
+                np.array(input_image),
+                100,
+                200,
+            )
+            Image.fromarray(result).save(fp=img_dest)
+            return result, img_dest
+        case "openpose":
+            openpose = PreprocessorModel("openpose")
+            result = openpose(np.array(input_image))
+            Image.fromarray(result[0]).save(fp=img_dest)
+            return result, img_dest
+        case "zoedepth":
+            zoedepth = PreprocessorModel("ZoeDepth")
+            result = zoedepth(np.array(input_image))
+            Image.fromarray(result).save(fp=img_dest)
+            return result, img_dest
+        case "scribble":
+            input_image.save(fp=img_dest)
+            return input_image, img_dest
+        case _:
+            return None, None

apps/shark_studio/api/initializers.py

@@ -0,0 +1,130 @@
+import importlib
+import os
+import signal
+import sys
+import warnings
+import json
+from threading import Thread
+
+from apps.shark_studio.modules.timer import startup_timer
+
+from apps.shark_studio.web.utils.tmp_configs import (
+    config_tmp,
+    clear_tmp_mlir,
+    clear_tmp_imgs,
+    shark_tmp,
+)
+
+
+def imports():
+    import torch  # noqa: F401
+
+    startup_timer.record("import torch")
+    warnings.filterwarnings(
+        action="ignore", category=DeprecationWarning, module="torch"
+    )
+    warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
+    warnings.filterwarnings(action="ignore", category=UserWarning, module="torch")
+    warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
+    warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
+    warnings.filterwarnings(
+        action="ignore", category=FutureWarning, module="huggingface-hub"
+    )
+    warnings.filterwarnings(
+        action="ignore", category=UserWarning, module="huggingface-hub"
+    )
+
+    # import gradio  # noqa: F401
+
+    # startup_timer.record("import gradio")
+
+    import apps.shark_studio.web.utils.globals as global_obj
+
+    global_obj._init()
+    startup_timer.record("initialize globals")
+
+    from apps.shark_studio.modules import (
+        img_processing,
+    )  # noqa: F401
+
+    startup_timer.record("other imports")
+
+
+def initialize():
+    configure_sigint_handler()
+    # Setup to use shark_tmp for gradio's temporary image files and clear any
+    # existing temporary images there if they exist. Then we can import gradio.
+    # It has to be in this order or gradio ignores what we've set up.
+
+    # config_tmp()
+    # clear_tmp_imgs()
+
+    from apps.shark_studio.web.utils.file_utils import (
+        create_model_folders,
+    )
+
+    # Create custom models folders if they don't exist
+    create_model_folders()
+
+    # initialize_rest(reload_script_modules=False)
+
+
+def initialize_rest(*, reload_script_modules=False):
+    """
+    Called both from initialize() and when reloading the webui.
+    """
+    # Keep this for adding reload options to the webUI.
+
+
+def dumpstacks():
+    import threading
+    import traceback
+
+    id2name = {th.ident: th.name for th in threading.enumerate()}
+    code = []
+    for threadId, stack in sys._current_frames().items():
+        code.append(f"\n# Thread: {id2name.get(threadId, '')}({threadId})")
+        for filename, lineno, name, line in traceback.extract_stack(stack):
+            code.append(f"""File: "{filename}", line {lineno}, in {name}""")
+            if line:
+                code.append("  " + line.strip())
+    with open(os.path.join(shark_tmp, "stack_dump.log"), "w") as f:
+        f.write("\n".join(code))
+
+
+def setup_middleware(app):
+    from starlette.middleware.gzip import GZipMiddleware
+
+    app.middleware_stack = (
+        None  # reset current middleware to allow modifying user provided list
+    )
+    app.add_middleware(GZipMiddleware, minimum_size=1000)
+    configure_cors_middleware(app)
+    app.build_middleware_stack()  # rebuild middleware stack on-the-fly
+
+
+def configure_cors_middleware(app):
+    from starlette.middleware.cors import CORSMiddleware
+    from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+
+    cors_options = {
+        "allow_methods": ["*"],
+        "allow_headers": ["*"],
+        "allow_credentials": True,
+    }
+    if cmd_opts.api_accept_origin:
+        cors_options["allow_origins"] = cmd_opts.api_accept_origin.split(",")
+
+    app.add_middleware(CORSMiddleware, **cors_options)
+
+
+def configure_sigint_handler():
+    # make the program just exit at ctrl+c without waiting for anything
+    def sigint_handler(sig, frame):
+        print(f"Interrupted with signal {sig} in {frame}")
+
+        dumpstacks()
+
+        os._exit(0)
+
+    signal.signal(signal.SIGINT, sigint_handler)

apps/shark_studio/api/llm.py

@@ -0,0 +1,475 @@
+from turbine_models.custom_models import stateless_llama
+from turbine_models.model_runner import vmfbRunner
+from turbine_models.gen_external_params.gen_external_params import gen_external_params
+import time
+from shark.iree_utils.compile_utils import compile_module_to_flatbuffer
+from apps.shark_studio.web.utils.file_utils import (
+    get_resource_path,
+    get_checkpoints_path,
+)
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+from apps.shark_studio.api.utils import parse_device
+from urllib.request import urlopen
+import iree.runtime as ireert
+from itertools import chain
+import gc
+import os
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+llm_model_map = {
+    "meta-llama/Llama-2-7b-chat-hf": {
+        "initializer": stateless_llama.export_transformer_model,
+        "hf_model_name": "meta-llama/Llama-2-7b-chat-hf",
+        "compile_flags": ["--iree-opt-const-expr-hoisting=False"],
+        "stop_token": 2,
+        "max_tokens": 4096,
+        "system_prompt": """<s>[INST] <<SYS>>Be concise. You are a helpful, respectful and honest assistant. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>>""",
+    },
+    "Trelis/Llama-2-7b-chat-hf-function-calling-v2": {
+        "initializer": stateless_llama.export_transformer_model,
+        "hf_model_name": "Trelis/Llama-2-7b-chat-hf-function-calling-v2",
+        "compile_flags": ["--iree-opt-const-expr-hoisting=False"],
+        "stop_token": 2,
+        "max_tokens": 4096,
+        "system_prompt": """<s>[INST] <<SYS>>Be concise. You are a helpful, respectful and honest assistant. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>>""",
+    },
+    "TinyPixel/small-llama2": {
+        "initializer": stateless_llama.export_transformer_model,
+        "hf_model_name": "TinyPixel/small-llama2",
+        "compile_flags": ["--iree-opt-const-expr-hoisting=True"],
+        "stop_token": 2,
+        "max_tokens": 1024,
+        "system_prompt": """<s>[INST] <<SYS>>Be concise. You are a helpful, respectful and honest assistant. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>>""",
+    },
+}
+
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<s>", "</s>"
+
+DEFAULT_CHAT_SYS_PROMPT = """<s>[INST] <<SYS>>
+Be concise. You are a helpful, respectful and honest assistant. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n <</SYS>>\n\n
+"""
+
+
+def append_user_prompt(history, input_prompt):
+    user_prompt = f"{B_INST} {input_prompt} {E_INST}"
+    history += user_prompt
+    return history
+
+
+class LanguageModel:
+    def __init__(
+        self,
+        model_name,
+        hf_auth_token=None,
+        device=None,
+        quantization="int4",
+        precision="",
+        external_weights=None,
+        use_system_prompt=True,
+        streaming_llm=False,
+    ):
+        _, _, self.triple = parse_device(device)
+        self.hf_model_name = llm_model_map[model_name]["hf_model_name"]
+        self.device = device.split("=>")[-1].strip()
+        self.backend = self.device.split("://")[0]
+        self.driver = self.backend
+        if "cpu" in device:
+            self.device = "cpu"
+            self.backend = "llvm-cpu"
+            self.driver = "local-task"
+
+        print(f"Selected {self.backend} as IREE target backend.")
+        self.precision = "f32" if "cpu" in device else "f16"
+        self.quantization = quantization
+        self.safe_name = self.hf_model_name.replace("/", "_").replace("-", "_")
+        self.external_weight_file = None
+        # TODO: find a programmatic solution for model arch spec instead of hardcoding llama2
+        self.file_spec = "_".join(
+            [
+                self.safe_name,
+                self.precision,
+            ]
+        )
+        if self.quantization != "None":
+            self.file_spec += "_" + self.quantization
+
+        if external_weights in ["safetensors", "gguf"]:
+            self.external_weight_file = get_resource_path(
+                os.path.join("..", self.file_spec + "." + external_weights)
+            )
+        else:
+            self.external_weights = None
+            self.external_weight_file = None
+
+        if streaming_llm:
+            # Add streaming suffix to file spec after setting external weights filename.
+            self.file_spec += "_streaming"
+        self.streaming_llm = streaming_llm
+
+        self.tempfile_name = get_resource_path(
+            os.path.join("..", f"{self.file_spec}.tempfile")
+        )
+        # TODO: Tag vmfb with target triple of device instead of HAL backend
+        self.vmfb_name = str(
+            get_resource_path(
+                os.path.join("..", f"{self.file_spec}_{self.backend}.vmfb.tempfile")
+            )
+        )
+
+        self.max_tokens = llm_model_map[model_name]["max_tokens"]
+        self.iree_module_dict = None
+        self.use_system_prompt = use_system_prompt
+        self.global_iter = 0
+        self.prev_token_len = 0
+        self.first_input = True
+        self.hf_auth_token = hf_auth_token
+        if self.external_weight_file is not None:
+            if not os.path.exists(self.external_weight_file):
+                print(
+                    f"External weight file {self.external_weight_file} does not exist. Generating..."
+                )
+                gen_external_params(
+                    hf_model_name=self.hf_model_name,
+                    quantization=self.quantization,
+                    weight_path=self.external_weight_file,
+                    hf_auth_token=hf_auth_token,
+                    precision=self.precision,
+                )
+            else:
+                print(
+                    f"External weight file {self.external_weight_file} found for {self.vmfb_name}"
+                )
+            self.external_weight_file = str(self.external_weight_file)
+
+        if os.path.exists(self.vmfb_name) and (
+            external_weights is None or os.path.exists(str(self.external_weight_file))
+        ):
+            self.runner = vmfbRunner(
+                device=self.driver,
+                vmfb_path=self.vmfb_name,
+                external_weight_path=self.external_weight_file,
+            )
+            if self.streaming_llm:
+                self.model = self.runner.ctx.modules.streaming_state_update
+            else:
+                self.model = self.runner.ctx.modules.state_update
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.hf_model_name,
+                use_fast=False,
+                use_auth_token=hf_auth_token,
+            )
+        elif not os.path.exists(self.tempfile_name):
+            self.torch_ir, self.tokenizer = llm_model_map[self.hf_model_name][
+                "initializer"
+            ](
+                self.hf_model_name,
+                hf_auth_token,
+                compile_to="torch",
+                external_weights=external_weights,
+                precision=self.precision,
+                quantization=self.quantization,
+                streaming_llm=self.streaming_llm,
+                decomp_attn=True,
+            )
+            with open(self.tempfile_name, "w+") as f:
+                f.write(self.torch_ir)
+            del self.torch_ir
+            gc.collect()
+            self.compile()
+        else:
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.hf_model_name,
+                use_fast=False,
+                use_auth_token=hf_auth_token,
+            )
+            self.compile()
+        # Reserved for running HF torch model as reference.
+        self.hf_mod = None
+
+    def compile(self) -> None:
+        # this comes with keys: "vmfb", "config", and "temp_file_to_unlink".
+        # ONLY architecture/api-specific compile-time flags for each backend, if needed.
+        # hf_model_id-specific global flags currently in model map.
+        flags = []
+        if "cpu" in self.backend:
+            flags.extend(
+                [
+                    "--iree-global-opt-enable-quantized-matmul-reassociation",
+                ]
+            )
+        elif self.backend == "vulkan":
+            flags.extend(["--iree-stream-resource-max-allocation-size=4294967296"])
+        elif self.backend == "rocm":
+            flags.extend(
+                [
+                    "--iree-codegen-llvmgpu-enable-transform-dialect-jit=false",
+                    "--iree-llvmgpu-enable-prefetch=true",
+                    "--iree-opt-outer-dim-concat=true",
+                    "--iree-flow-enable-aggressive-fusion",
+                ]
+            )
+            if "gfx9" in self.triple:
+                flags.extend(
+                    [
+                        f"--iree-codegen-transform-dialect-library={get_mfma_spec_path(self.triple, get_checkpoints_path())}",
+                        "--iree-codegen-llvmgpu-use-vector-distribution=true",
+                    ]
+                )
+        flags.extend(llm_model_map[self.hf_model_name]["compile_flags"])
+        flatbuffer_blob = compile_module_to_flatbuffer(
+            self.tempfile_name,
+            device=self.device,
+            frontend="auto",
+            model_config_path=None,
+            extra_args=flags,
+            write_to=self.vmfb_name,
+        )
+        self.runner = vmfbRunner(
+            device=self.driver,
+            vmfb_path=self.vmfb_name,
+            external_weight_path=self.external_weight_file,
+        )
+        if self.streaming_llm:
+            self.model = self.runner.ctx.modules.streaming_state_update
+        else:
+            self.model = self.runner.ctx.modules.state_update
+
+    def sanitize_prompt(self, prompt):
+        if isinstance(prompt, list):
+            prompt = list(chain.from_iterable(prompt))
+            prompt = " ".join([x for x in prompt if isinstance(x, str)])
+        prompt = prompt.replace("\n", " ")
+        prompt = prompt.replace("\t", " ")
+        prompt = prompt.replace("\r", " ")
+        if self.use_system_prompt and self.global_iter == 0:
+            prompt = append_user_prompt(DEFAULT_CHAT_SYS_PROMPT, prompt)
+            return prompt
+        else:
+            return f"{B_INST} {prompt} {E_INST}"
+
+    def chat(self, prompt):
+        prompt = self.sanitize_prompt(prompt)
+
+        input_tensor = self.tokenizer(prompt, return_tensors="pt").input_ids
+
+        def format_out(results):
+            return torch.tensor(results.to_host()[0][0])
+
+        history = []
+        for iter in range(self.max_tokens):
+            if self.streaming_llm:
+                token_slice = max(self.prev_token_len - 1, 0)
+                input_tensor = input_tensor[:, token_slice:]
+            if self.streaming_llm and self.model["get_seq_step"]() > 600:
+                print("Evicting cache space!")
+                self.model["evict_kvcache_space"]()
+            token_len = input_tensor.shape[-1]
+            device_inputs = [
+                ireert.asdevicearray(self.runner.config.device, input_tensor)
+            ]
+            if self.first_input or not self.streaming_llm:
+                st_time = time.time()
+                token = self.model["run_initialize"](*device_inputs)
+                total_time = time.time() - st_time
+                token_len += 1
+                self.first_input = False
+            else:
+                st_time = time.time()
+                token = self.model["run_cached_initialize"](*device_inputs)
+                total_time = time.time() - st_time
+                token_len += 1
+
+            history.append(format_out(token))
+            while (
+                format_out(token) != llm_model_map[self.hf_model_name]["stop_token"]
+                and len(history) < self.max_tokens
+            ):
+                dec_time = time.time()
+                if self.streaming_llm and self.model["get_seq_step"]() > 600:
+                    print("Evicting cache space!")
+                    self.model["evict_kvcache_space"]()
+                token = self.model["run_forward"](token)
+                history.append(format_out(token))
+                total_time = time.time() - dec_time
+                yield self.tokenizer.decode(history), total_time
+
+            self.prev_token_len = token_len + len(history)
+
+            if format_out(token) == llm_model_map[self.hf_model_name]["stop_token"]:
+                break
+
+        for i in range(len(history)):
+            if type(history[i]) != int:
+                history[i] = int(history[i])
+        result_output = self.tokenizer.decode(history)
+        self.global_iter += 1
+        return result_output, total_time
+
+    # Reference HF model function for sanity checks.
+    def chat_hf(self, prompt):
+        if self.hf_mod is None:
+            self.hf_mod = AutoModelForCausalLM.from_pretrained(
+                self.hf_model_name,
+                torch_dtype=torch.float,
+                token=self.hf_auth_token,
+            )
+        prompt = self.sanitize_prompt(prompt)
+
+        input_tensor = self.tokenizer(prompt, return_tensors="pt").input_ids
+        history = []
+        for iter in range(self.max_tokens):
+            token_len = input_tensor.shape[-1]
+            if self.first_input:
+                st_time = time.time()
+                result = self.hf_mod(input_tensor)
+                token = torch.argmax(result.logits[:, -1, :], dim=1)
+                total_time = time.time() - st_time
+                token_len += 1
+                pkv = result.past_key_values
+                self.first_input = False
+
+            history.append(int(token))
+            while token != llm_model_map[self.hf_model_name]["stop_token"]:
+                dec_time = time.time()
+                result = self.hf_mod(token.reshape([1, 1]), past_key_values=pkv)
+                history.append(int(token))
+                total_time = time.time() - dec_time
+                token = torch.argmax(result.logits[:, -1, :], dim=1)
+                pkv = result.past_key_values
+                yield self.tokenizer.decode(history), total_time
+
+            self.prev_token_len = token_len + len(history)
+
+            if token == llm_model_map[self.hf_model_name]["stop_token"]:
+                break
+        for i in range(len(history)):
+            if type(history[i]) != int:
+                history[i] = int(history[i])
+        result_output = self.tokenizer.decode(history)
+        self.global_iter += 1
+        return result_output, total_time
+
+
+def get_mfma_spec_path(target_chip, save_dir):
+    url = "https://raw.githubusercontent.com/iree-org/iree/main/build_tools/pkgci/external_test_suite/attention_and_matmul_spec.mlir"
+    attn_spec = urlopen(url).read().decode("utf-8")
+    spec_path = os.path.join(save_dir, "attention_and_matmul_spec_mfma.mlir")
+    if os.path.exists(spec_path):
+        return spec_path
+    with open(spec_path, "w") as f:
+        f.write(attn_spec)
+    return spec_path
+
+
+def llm_chat_api(InputData: dict):
+    from datetime import datetime as dt
+
+    import apps.shark_studio.web.utils.globals as global_obj
+
+    print(f"Input keys : {InputData.keys()}")
+
+    # print(f"model : {InputData['model']}")
+
+    is_chat_completion_api = (
+        "messages" in InputData.keys()
+    )  # else it is the legacy `completion` api
+
+    # For Debugging input data from API
+    if is_chat_completion_api:
+        print(f"message -> role : {InputData['messages'][0]['role']}")
+        print(f"message -> content : {InputData['messages'][0]['content']}")
+    else:
+        print(f"prompt : {InputData['prompt']}")
+
+    model_name = (
+        InputData["model"]
+        if "model" in InputData.keys()
+        else "meta-llama/Llama-2-7b-chat-hf"
+    )
+    model_path = llm_model_map[model_name]
+    device = InputData["device"] if "device" in InputData.keys() else "cpu"
+    precision = "fp16"
+    max_tokens = InputData["max_tokens"] if "max_tokens" in InputData.keys() else 4096
+
+    device_id = None
+    if not global_obj.get_llm_obj():
+        print("\n[LOG] Initializing new pipeline...")
+        global_obj.clear_cache()
+        gc.collect()
+        if "cuda" in device:
+            device = "cuda"
+        elif "vulkan" in device:
+            device_id = int(device.split("://")[1])
+            device = "vulkan"
+        elif "cpu" in device:
+            device = "cpu"
+            precision = "fp32"
+        else:
+            print("unrecognized device")
+        llm_model = LanguageModel(
+            model_name=model_name,
+            hf_auth_token=cmd_opts.hf_auth_token,
+            device=device,
+            quantization=cmd_opts.quantization,
+            external_weights="safetensors",
+            use_system_prompt=True,
+            streaming_llm=False,
+        )
+        global_obj.set_llm_obj(llm_model)
+    else:
+        llm_model = global_obj.get_llm_obj()
+
+    llm_model.max_tokens = max_tokens
+    # TODO: add role dict for different models
+    if is_chat_completion_api:
+        # TODO: add funtionality for multiple messages
+        prompt = append_user_prompt(
+            InputData["messages"][0]["role"], InputData["messages"][0]["content"]
+        )
+    else:
+        prompt = InputData["prompt"]
+    print("prompt = ", prompt)
+
+    for res_op, _ in llm_model.chat(prompt):
+        if is_chat_completion_api:
+            choices = [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": res_op,  # since we are yeilding the result
+                    },
+                    "finish_reason": "stop",  # or length
+                }
+            ]
+        else:
+            choices = [
+                {
+                    "text": res_op,
+                    "index": 0,
+                    "logprobs": None,
+                    "finish_reason": "stop",  # or length
+                }
+            ]
+    end_time = dt.now().strftime("%Y%m%d%H%M%S%f")
+    return {
+        "id": end_time,
+        "object": "chat.completion" if is_chat_completion_api else "text_completion",
+        "created": int(end_time),
+        "choices": choices,
+    }
+
+
+if __name__ == "__main__":
+    lm = LanguageModel(
+        "Trelis/Llama-2-7b-chat-hf-function-calling-v2",
+        hf_auth_token=None,
+        device="cpu-task",
+        external_weights="safetensors",
+    )
+
+    print("model loaded")
+    for i in lm.chat("hi, what are you?"):
+        print(i)

apps/shark_studio/api/sd.py

@@ -0,0 +1,579 @@
+import gc
+import torch
+import gradio as gr
+import time
+import os
+import json
+import numpy as np
+import copy
+import importlib.util
+import sys
+from tqdm.auto import tqdm
+
+from pathlib import Path
+from random import randint
+
+
+from apps.shark_studio.api.controlnet import control_adapter_map
+from apps.shark_studio.api.utils import parse_device
+from apps.shark_studio.web.utils.state import status_label
+from apps.shark_studio.web.utils.file_utils import (
+    safe_name,
+    get_resource_path,
+    get_checkpoints_path,
+)
+
+from apps.shark_studio.modules.img_processing import (
+    save_output_img,
+)
+
+
+from subprocess import check_output
+
+EMPTY_SD_MAP = {
+    "clip": None,
+    "scheduler": None,
+    "unet": None,
+    "vae_decode": None,
+}
+
+EMPTY_SDXL_MAP = {
+    "prompt_encoder": None,
+    "scheduled_unet": None,
+    "vae_decode": None,
+    "pipeline": None,
+    "full_pipeline": None,
+}
+
+EMPTY_FLAGS = {
+    "clip": None,
+    "unet": None,
+    "vae": None,
+    "pipeline": None,
+}
+
+
+def load_script(source, module_name):
+    """
+    reads file source and loads it as a module
+
+    :param source: file to load
+    :param module_name: name of module to register in sys.modules
+    :return: loaded module
+    """
+    spec = importlib.util.spec_from_file_location(module_name, source)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)
+
+    return module
+
+
+class StableDiffusion:
+    # This class is responsible for executing image generation and creating
+    # /managing a set of compiled modules to run Stable Diffusion. The init
+    # aims to be as general as possible, and the class will infer and compile
+    # a list of necessary modules or a combined "pipeline module" for a
+    # specified job based on the inference task.
+
+    def __init__(
+        self,
+        base_model_id,
+        height: int,
+        width: int,
+        batch_size: int,
+        steps: int,
+        scheduler: str,
+        precision: str,
+        device: str,
+        target_triple: str = None,
+        custom_vae: str = None,
+        num_loras: int = 0,
+        import_ir: bool = True,
+        is_controlled: bool = False,
+        external_weights: str = "safetensors",
+        progress=gr.Progress(),
+    ):
+        progress(0, desc="Initializing pipeline...")
+        self.ui_device = device
+        self.precision = precision
+        self.compiled_pipeline = False
+        self.base_model_id = base_model_id
+        self.custom_vae = custom_vae
+        self.is_sdxl = "xl" in self.base_model_id.lower()
+        self.is_custom = ".py" in self.base_model_id.lower()
+        if self.is_custom:
+            custom_module = load_script(
+                os.path.join(get_checkpoints_path("scripts"), self.base_model_id),
+                "custom_pipeline",
+            )
+            self.turbine_pipe = custom_module.StudioPipeline
+            self.dynamic_steps = False
+            self.model_map = custom_module.MODEL_MAP
+        elif self.is_sdxl:
+            from turbine_models.custom_models.sdxl_inference.sdxl_compiled_pipeline import (
+                SharkSDXLPipeline,
+            )
+
+            self.turbine_pipe = SharkSDXLPipeline
+            self.dynamic_steps = False
+            self.model_map = EMPTY_SDXL_MAP
+        else:
+            from turbine_models.custom_models.sd_inference.sd_pipeline import (
+                SharkSDPipeline,
+            )
+
+            self.turbine_pipe = SharkSDPipeline
+            self.dynamic_steps = True
+            self.model_map = EMPTY_SD_MAP
+        max_length = 64
+        target_backend, self.rt_device, triple = parse_device(device, target_triple)
+        pipe_id_list = [
+            safe_name(base_model_id),
+            str(batch_size),
+            str(max_length),
+            f"{str(height)}x{str(width)}",
+            precision,
+            triple,
+        ]
+        if num_loras > 0:
+            pipe_id_list.append(str(num_loras) + "lora")
+        if is_controlled:
+            pipe_id_list.append("controlled")
+        if custom_vae:
+            pipe_id_list.append(custom_vae)
+        self.pipe_id = "_".join(pipe_id_list)
+        self.pipeline_dir = Path(os.path.join(get_checkpoints_path(), self.pipe_id))
+        self.weights_path = Path(
+            os.path.join(
+                get_checkpoints_path(), safe_name(self.base_model_id + "_" + precision)
+            )
+        )
+        if not os.path.exists(self.weights_path):
+            os.mkdir(self.weights_path)
+
+        decomp_attn = True
+        attn_spec = None
+        if triple in ["gfx940", "gfx942", "gfx90a"]:
+            decomp_attn = False
+            attn_spec = "mfma"
+        elif triple in ["gfx1100", "gfx1103", "gfx1150"]:
+            decomp_attn = False
+            attn_spec = "wmma"
+            if triple in ["gfx1103", "gfx1150"]:
+                # external weights have issues on igpu
+                external_weights = None
+        elif target_backend == "llvm-cpu":
+            decomp_attn = False
+        progress(0.5, desc="Initializing pipeline...")
+        self.sd_pipe = self.turbine_pipe(
+            hf_model_name=base_model_id,
+            scheduler_id=scheduler,
+            height=height,
+            width=width,
+            precision=precision,
+            max_length=max_length,
+            batch_size=batch_size,
+            num_inference_steps=steps,
+            device=target_backend,
+            iree_target_triple=triple,
+            ireec_flags=EMPTY_FLAGS,
+            attn_spec=attn_spec,
+            decomp_attn=decomp_attn,
+            pipeline_dir=self.pipeline_dir,
+            external_weights_dir=self.weights_path,
+            external_weights=external_weights,
+            custom_vae=custom_vae,
+        )
+        progress(1, desc="Pipeline initialized!...")
+        gc.collect()
+
+    def prepare_pipe(
+        self,
+        custom_weights,
+        adapters,
+        embeddings,
+        is_img2img,
+        compiled_pipeline,
+        progress=gr.Progress(),
+    ):
+        progress(0, desc="Preparing models...")
+
+        self.is_img2img = False
+        mlirs = copy.deepcopy(self.model_map)
+        vmfbs = copy.deepcopy(self.model_map)
+        weights = copy.deepcopy(self.model_map)
+        if not self.is_sdxl:
+            compiled_pipeline = False
+        self.compiled_pipeline = compiled_pipeline
+
+        if custom_weights:
+            from apps.shark_studio.modules.ckpt_processing import (
+                preprocessCKPT,
+                save_irpa,
+            )
+
+            custom_weights = os.path.join(
+                get_checkpoints_path("checkpoints"),
+                safe_name(self.base_model_id.split("/")[-1]),
+                custom_weights,
+            )
+            diffusers_weights_path = preprocessCKPT(custom_weights, self.precision)
+            for key in weights:
+                if key in ["scheduled_unet", "unet"]:
+                    unet_weights_path = os.path.join(
+                        diffusers_weights_path,
+                        "unet",
+                        "diffusion_pytorch_model.safetensors",
+                    )
+                    weights[key] = save_irpa(unet_weights_path, "unet.")
+                if key in ["mmdit"]:
+                    mmdit_weights_path = os.path.join(
+                        diffusers_weights_path,
+                        "mmdit",
+                        "diffusion_pytorch_model_fp16.safetensors",
+                    )
+                    weights[key] = save_irpa(mmdit_weights_path, "mmdit.")
+                elif key in ["clip", "prompt_encoder", "text_encoder"]:
+                    if not self.is_sdxl and not self.is_custom:
+                        sd1_path = os.path.join(
+                            diffusers_weights_path, "text_encoder", "model.safetensors"
+                        )
+                        weights[key] = save_irpa(sd1_path, "text_encoder_model.")
+                    elif self.is_sdxl:
+                        clip_1_path = os.path.join(
+                            diffusers_weights_path, "text_encoder", "model.safetensors"
+                        )
+                        clip_2_path = os.path.join(
+                            diffusers_weights_path,
+                            "text_encoder_2",
+                            "model.safetensors",
+                        )
+                        weights[key] = [
+                            save_irpa(clip_1_path, "text_encoder_model_1."),
+                            save_irpa(clip_2_path, "text_encoder_model_2."),
+                        ]
+                    elif self.is_custom:
+                        clip_g_path = os.path.join(
+                            diffusers_weights_path,
+                            "text_encoder",
+                            "model.fp16.safetensors",
+                        )
+                        clip_l_path = os.path.join(
+                            diffusers_weights_path,
+                            "text_encoder_2",
+                            "model.fp16.safetensors",
+                        )
+                        t5xxl_path = os.path.join(
+                            diffusers_weights_path,
+                            "text_encoder_3",
+                            "model.fp16.safetensors",
+                        )
+                        weights[key] = [
+                            save_irpa(clip_g_path, "clip_g.transformer."),
+                            save_irpa(clip_l_path, "clip_l.transformer."),
+                            save_irpa(t5xxl_path, "t5xxl.transformer."),
+                        ]
+                elif key in ["vae_decode"] and weights[key] is None:
+                    vae_weights_path = os.path.join(
+                        diffusers_weights_path,
+                        "vae",
+                        "diffusion_pytorch_model.safetensors",
+                    )
+                    weights[key] = save_irpa(vae_weights_path, "vae.")
+
+        progress(0.25, desc=f"Preparing pipeline for {self.ui_device}...")
+
+        vmfbs, weights = self.sd_pipe.check_prepared(
+            mlirs, vmfbs, weights, interactive=False
+        )
+        progress(0.5, desc=f"Artifacts ready!")
+        progress(0.75, desc=f"Loading models and weights...")
+
+        self.sd_pipe.load_pipeline(
+            vmfbs, weights, self.rt_device, self.compiled_pipeline
+        )
+        progress(1, desc="Pipeline loaded! Generating images...")
+        return
+
+    def generate_images(
+        self,
+        prompt,
+        negative_prompt,
+        image,
+        strength,
+        guidance_scale,
+        seed,
+        ondemand,
+        resample_type,
+        control_mode,
+        hints,
+        progress=gr.Progress(),
+    ):
+
+        img = self.sd_pipe.generate_images(
+            prompt,
+            negative_prompt,
+            1,
+            guidance_scale,
+            seed,
+            return_imgs=True,
+        )
+        return img
+
+
+def shark_sd_fn(
+    prompt,
+    negative_prompt,
+    sd_init_image: list,
+    height: int,
+    width: int,
+    steps: int,
+    strength: float,
+    guidance_scale: float,
+    seed: list,
+    batch_count: int,
+    batch_size: int,
+    scheduler: str,
+    base_model_id: str,
+    custom_weights: str,
+    custom_vae: str,
+    precision: str,
+    device: str,
+    target_triple: str,
+    ondemand: bool,
+    compiled_pipeline: bool,
+    resample_type: str,
+    controlnets: dict,
+    embeddings: dict,
+    seed_increment: str | int = 1,
+    output_type: str = "png",
+    # progress=gr.Progress(),
+):
+    sd_kwargs = locals()
+    if not isinstance(sd_init_image, list):
+        sd_init_image = [sd_init_image]
+    is_img2img = True if sd_init_image[0] is not None else False
+
+    from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+    import apps.shark_studio.web.utils.globals as global_obj
+
+    adapters = {}
+    is_controlled = False
+    control_mode = None
+    hints = []
+    num_loras = 0
+    import_ir = True
+    for i in embeddings:
+        num_loras += 1 if embeddings[i] else 0
+    if "model" in controlnets:
+        for i, model in enumerate(controlnets["model"]):
+            if "xl" not in base_model_id.lower():
+                adapters[f"control_adapter_{model}"] = {
+                    "hf_id": control_adapter_map["runwayml/stable-diffusion-v1-5"][
+                        model
+                    ],
+                    "strength": controlnets["strength"][i],
+                }
+            else:
+                adapters[f"control_adapter_{model}"] = {
+                    "hf_id": control_adapter_map["stabilityai/stable-diffusion-xl-1.0"][
+                        model
+                    ],
+                    "strength": controlnets["strength"][i],
+                }
+            if model is not None:
+                is_controlled = True
+        control_mode = controlnets["control_mode"]
+        for i in controlnets["hint"]:
+            hints.append[i]
+
+    submit_pipe_kwargs = {
+        "base_model_id": base_model_id,
+        "height": height,
+        "width": width,
+        "batch_size": batch_size,
+        "precision": precision,
+        "device": device,
+        "target_triple": target_triple,
+        "custom_vae": custom_vae,
+        "num_loras": num_loras,
+        "import_ir": import_ir,
+        "is_controlled": is_controlled,
+        "steps": steps,
+        "scheduler": scheduler,
+    }
+    submit_prep_kwargs = {
+        "custom_weights": custom_weights,
+        "adapters": adapters,
+        "embeddings": embeddings,
+        "is_img2img": is_img2img,
+        "compiled_pipeline": compiled_pipeline,
+    }
+    submit_run_kwargs = {
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "image": sd_init_image,
+        "strength": strength,
+        "guidance_scale": guidance_scale,
+        "seed": seed,
+        "ondemand": ondemand,
+        "resample_type": resample_type,
+        "control_mode": control_mode,
+        "hints": hints,
+    }
+    if global_obj.get_sd_obj() and global_obj.get_sd_obj().dynamic_steps:
+        submit_run_kwargs["steps"] = submit_pipe_kwargs["steps"]
+        submit_pipe_kwargs.pop("steps")
+    if (
+        not global_obj.get_sd_obj()
+        or global_obj.get_pipe_kwargs() != submit_pipe_kwargs
+    ):
+        print("\n[LOG] Initializing new pipeline...")
+        global_obj.clear_cache()
+        gc.collect()
+
+        # Initializes the pipeline and retrieves IR based on all
+        # parameters that are static in the turbine output format,
+        # which is currently MLIR in the torch dialect.
+
+        sd_pipe = StableDiffusion(
+            **submit_pipe_kwargs,
+        )
+        global_obj.set_sd_obj(sd_pipe)
+        global_obj.set_pipe_kwargs(submit_pipe_kwargs)
+    if (
+        not global_obj.get_prep_kwargs()
+        or global_obj.get_prep_kwargs() != submit_prep_kwargs
+    ):
+        global_obj.set_prep_kwargs(submit_prep_kwargs)
+        global_obj.get_sd_obj().prepare_pipe(**submit_prep_kwargs)
+
+    generated_imgs = []
+    if submit_run_kwargs["seed"] in [-1, "-1"]:
+        submit_run_kwargs["seed"] = randint(0, 4294967295)
+        seed_increment = "random"
+        # print(f"\n[LOG] Random seed: {seed}")
+    # progress(None, desc=f"Generating...")
+
+    for current_batch in range(batch_count):
+        start_time = time.time()
+        out_imgs = global_obj.get_sd_obj().generate_images(**submit_run_kwargs)
+        if not isinstance(out_imgs, list):
+            out_imgs = [out_imgs]
+        # total_time = time.time() - start_time
+        # text_output = f"Total image(s) generation time: {total_time:.4f}sec"
+        # print(f"\n[LOG] {text_output}")
+        # if global_obj.get_sd_status() == SD_STATE_CANCEL:
+        #     break
+        # else:
+        for batch in range(batch_size):
+            if output_type == "png":
+                save_output_img(
+                    out_imgs[batch],
+                    seed,
+                    sd_kwargs,
+                )
+        generated_imgs.extend(out_imgs)
+
+        yield generated_imgs, status_label(
+            "Stable Diffusion", current_batch + 1, batch_count, batch_size
+        )
+        if batch_count > 1:
+            submit_run_kwargs["seed"] = get_next_seed(seed, seed_increment)
+
+    return (generated_imgs, "")
+
+
+def shark_sd_fn_dict_input(sd_kwargs: dict, *, progress=gr.Progress()):
+    print("\n[LOG] Submitting Request...")
+
+    for key in sd_kwargs:
+        if sd_kwargs[key] in [None, []]:
+            sd_kwargs[key] = None
+        if sd_kwargs[key] in ["None"]:
+            sd_kwargs[key] = ""
+        if key in ["steps", "height", "width", "batch_count", "batch_size"]:
+            sd_kwargs[key] = int(sd_kwargs[key])
+        if key == "seed":
+            sd_kwargs[key] = int(sd_kwargs[key])
+
+    # TODO: move these checks into the UI code so we don't have gradio warnings in a generalized dict input function.
+    if not sd_kwargs["device"]:
+        gr.Warning("No device specified. Please specify a device.")
+        return None, ""
+    if sd_kwargs["height"] not in [512, 1024]:
+        gr.Warning("Height must be 512 or 1024. This is a temporary limitation.")
+        return None, ""
+    if sd_kwargs["height"] != sd_kwargs["width"]:
+        gr.Warning("Height and width must be the same. This is a temporary limitation.")
+        return None, ""
+    if sd_kwargs["base_model_id"] == "stabilityai/sdxl-turbo":
+        if sd_kwargs["steps"] > 10:
+            gr.Warning("Max steps for sdxl-turbo is 10. 1 to 4 steps are recommended.")
+            return None, ""
+        if sd_kwargs["guidance_scale"] > 3:
+            gr.Warning(
+                "sdxl-turbo CFG scale should be less than 2.0 if using negative prompt, 0 otherwise."
+            )
+            return None, ""
+    if sd_kwargs["target_triple"] == "":
+        if not parse_device(sd_kwargs["device"], sd_kwargs["target_triple"])[2]:
+            gr.Warning(
+                "Target device architecture could not be inferred. Please specify a target triple, e.g. 'gfx1100' for a Radeon 7900xtx."
+            )
+            return None, ""
+
+    generated_imgs = yield from shark_sd_fn(**sd_kwargs)
+    return generated_imgs
+
+
+def get_next_seed(seed, seed_increment: str | int = 10):
+    if isinstance(seed_increment, int):
+        # print(f"\n[LOG] Seed after batch increment: {seed + seed_increment}")
+        return int(seed + seed_increment)
+    elif seed_increment == "random":
+        seed = randint(0, 4294967295)
+        # print(f"\n[LOG] Random seed: {seed}")
+        return seed
+
+
+def unload_sd():
+    print("Unloading models.")
+    import apps.shark_studio.web.utils.globals as global_obj
+
+    global_obj.clear_cache()
+    gc.collect()
+
+
+def cancel_sd():
+    print("Inject call to cancel longer API calls.")
+    return
+
+
+def view_json_file(file_path):
+    content = ""
+    with open(file_path, "r") as fopen:
+        content = fopen.read()
+    return content
+
+
+def safe_name(name):
+    return name.replace("/", "_").replace("\\", "_").replace(".", "_")
+
+
+if __name__ == "__main__":
+    from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+    import apps.shark_studio.web.utils.globals as global_obj
+
+    global_obj._init()
+
+    sd_json = view_json_file(
+        get_resource_path(os.path.join(cmd_opts.config_dir, cmd_opts.default_config))
+    )
+    sd_kwargs = json.loads(sd_json)
+    # for arg in vars(cmd_opts):
+    #     if arg in sd_kwargs:
+    #         sd_kwargs[arg] = getattr(cmd_opts, arg)
+    for i in shark_sd_fn_dict_input(sd_kwargs):
+        print(i)

apps/shark_studio/api/utils.py

@@ -0,0 +1,288 @@
+import numpy as np
+import json
+from random import (
+    randint,
+    seed as seed_random,
+    getstate as random_getstate,
+    setstate as random_setstate,
+)
+
+from pathlib import Path
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+from cpuinfo import get_cpu_info
+
+
+def iree_device_map(device):
+    uri_parts = device.split("://", 2)
+    iree_driver = (
+        _IREE_DEVICE_MAP[uri_parts[0]]
+        if uri_parts[0] in _IREE_DEVICE_MAP
+        else uri_parts[0]
+    )
+    if len(uri_parts) == 1:
+        return iree_driver
+    elif "rocm" in uri_parts:
+        return "rocm"
+    else:
+        return f"{iree_driver}://{uri_parts[1]}"
+
+
+def get_supported_device_list():
+    return list(_IREE_DEVICE_MAP.keys())
+
+
+_IREE_DEVICE_MAP = {
+    "cpu": "local-task",
+    "cpu-task": "local-task",
+    "cpu-sync": "local-sync",
+    "cuda": "cuda",
+    "vulkan": "vulkan",
+    "metal": "metal",
+    "rocm": "rocm",
+    "hip": "hip",
+    "intel-gpu": "level_zero",
+}
+
+
+def iree_target_map(device):
+    if "://" in device:
+        device = device.split("://")[0]
+    return _IREE_TARGET_MAP[device] if device in _IREE_TARGET_MAP else device
+
+
+_IREE_TARGET_MAP = {
+    "cpu": "llvm-cpu",
+    "cpu-task": "llvm-cpu",
+    "cpu-sync": "llvm-cpu",
+    "cuda": "cuda",
+    "vulkan": "vulkan-spirv",
+    "metal": "metal",
+    "rocm": "rocm",
+    "hip": "rocm",
+    "intel-gpu": "opencl-spirv",
+}
+
+
+def get_available_devices():
+    return ["rocm", "cpu"]
+
+    def get_devices_by_name(driver_name):
+
+        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:
+            cpu_name = get_cpu_info()["brand_raw"]
+            for i, device in enumerate(device_list_dict):
+                device_name = (
+                    cpu_name if device["name"] == "default" else device["name"]
+                )
+                if "local" in driver_name:
+                    device_list.append(
+                        f"{device_name} => {driver_name.replace('local', 'cpu')}"
+                    )
+                else:
+                    # for drivers with single devices
+                    # let the default device be selected without any indexing
+                    if len(device_list_dict) == 1:
+                        device_list.append(f"{device_name} => {driver_name}")
+                    else:
+                        device_list.append(f"{device_name} => {driver_name}://{i}")
+        return device_list
+
+    # set_iree_runtime_flags()
+
+    available_devices = []
+    rocm_devices = get_devices_by_name("rocm")
+    available_devices.extend(rocm_devices)
+    # cpu_device = get_devices_by_name("cpu-sync")
+    # available_devices.extend(cpu_device)
+    cpu_device = get_devices_by_name("cpu-task")
+    available_devices.extend(cpu_device)
+
+    # from shark.iree_utils.vulkan_utils import (
+    #     get_all_vulkan_devices,
+    # )
+
+    # vulkaninfo_list = get_all_vulkan_devices()
+    # vulkan_devices = []
+    # id = 0
+    # for device in vulkaninfo_list:
+    #     vulkan_devices.append(f"{device.strip()} => vulkan://{id}")
+    #     id += 1
+    # if id != 0:
+    #     print(f"vulkan devices are available.")
+
+    # available_devices.extend(vulkan_devices)
+    # metal_devices = get_devices_by_name("metal")
+    # available_devices.extend(metal_devices)
+    # cuda_devices = get_devices_by_name("cuda")
+    # available_devices.extend(cuda_devices)
+    # hip_devices = get_devices_by_name("hip")
+    # available_devices.extend(hip_devices)
+
+    for idx, device_str in enumerate(available_devices):
+        if "AMD Radeon(TM) Graphics =>" in device_str:
+            igpu_id_candidates = [
+                x.split("w/")[-1].split("=>")[0]
+                for x in available_devices
+                if "M Graphics" in x
+            ]
+            for igpu_name in igpu_id_candidates:
+                if igpu_name:
+                    available_devices[idx] = device_str.replace(
+                        "AMD Radeon(TM) Graphics", igpu_name
+                    )
+                break
+    return available_devices
+
+
+def clean_device_info(raw_device):
+    # return appropriate device and device_id for consumption by Studio pipeline
+    # Multiple devices only supported for vulkan and rocm (as of now).
+    # default device must be selected for all others
+
+    device_id = None
+    device = raw_device if "=>" not in raw_device else raw_device.split("=>")[1].strip()
+    if "://" in device:
+        device, device_id = device.split("://")
+        if len(device_id) <= 2:
+            device_id = int(device_id)
+
+    if device not in ["hip", "rocm", "vulkan"]:
+        device_id = None
+    if device in ["hip", "rocm", "vulkan"] and device_id == None:
+        device_id = 0
+    return device, device_id
+
+
+def parse_device(device_str, target_override=""):
+
+    rt_driver, device_id = clean_device_info(device_str)
+    target_backend = iree_target_map(rt_driver)
+    if device_id:
+        rt_device = f"{rt_driver}://{device_id}"
+    else:
+        rt_device = rt_driver
+
+    if target_override:
+        if "cpu" in device_str:
+            rt_device = "local-task"
+        return target_backend, rt_device, target_override
+    match target_backend:
+        case "vulkan-spirv":
+            triple = get_iree_target_triple(device_str)
+            return target_backend, rt_device, triple
+        case "rocm":
+            triple = get_rocm_target_chip(device_str)
+            return target_backend, rt_device, triple
+        case "llvm-cpu":
+            if "Ryzen 9" in device_str:
+                return target_backend, "local-task", "znver4"
+            else:
+                return "llvm-cpu", "local-task", "x86_64-linux-gnu"
+
+
+def get_rocm_target_chip(device_str):
+    # TODO: Use a data file to map device_str to target chip.
+    rocm_chip_map = {
+        "6700": "gfx1031",
+        "6800": "gfx1030",
+        "6900": "gfx1030",
+        "7900": "gfx1100",
+        "MI300X": "gfx942",
+        "MI300A": "gfx940",
+        "MI210": "gfx90a",
+        "MI250": "gfx90a",
+        "MI100": "gfx908",
+        "MI50": "gfx906",
+        "MI60": "gfx906",
+        "780M": "gfx1103",
+    }
+    for key in rocm_chip_map:
+        if key in device_str:
+            return rocm_chip_map[key]
+    return None
+
+
+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"])
+    del driver
+    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.
+#     """
+
+#     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 get_opt_flags(model, precision="fp16"):
+#     iree_flags = []
+#     if len(cmd_opts.iree_vulkan_target_triple) > 0:
+#         iree_flags.append(
+#             f"-iree-vulkan-target-triple={cmd_opts.iree_vulkan_target_triple}"
+#         )
+#     if "rocm" in cmd_opts.device:
+#         from shark.iree_utils.gpu_utils import get_iree_rocm_args
+
+#         rocm_args = get_iree_rocm_args()
+#         iree_flags.extend(rocm_args)
+#     if cmd_opts.iree_constant_folding == False:
+#         iree_flags.append("--iree-opt-const-expr-hoisting=False")
+#         iree_flags.append(
+#             "--iree-codegen-linalg-max-constant-fold-elements=9223372036854775807"
+#         )
+#     if cmd_opts.data_tiling == False:
+#         iree_flags.append("--iree-opt-data-tiling=False")
+
+#     if "vae" not in model:
+#         # Due to lack of support for multi-reduce, we always collapse reduction
+#         # dims before dispatch formation right now.
+#         iree_flags += ["--iree-flow-collapse-reduction-dims"]
+#     return iree_flags

apps/shark_studio/modules/ckpt_processing.py

@@ -0,0 +1,152 @@
+import os
+import json
+import re
+import requests
+import torch
+import safetensors
+from shark_turbine.aot.params import (
+    ParameterArchiveBuilder,
+)
+from io import BytesIO
+from pathlib import Path
+from tqdm import tqdm
+from omegaconf import OmegaConf
+from diffusers import StableDiffusionPipeline
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
+    download_from_original_stable_diffusion_ckpt,
+    create_vae_diffusers_config,
+    convert_ldm_vae_checkpoint,
+)
+
+
+def get_path_to_diffusers_checkpoint(custom_weights, precision="fp16"):
+    path = Path(custom_weights)
+    diffusers_path = path.parent.absolute()
+    diffusers_directory_name = os.path.join("diffusers", path.stem + f"_{precision}")
+    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, precision="fp16", is_inpaint=False):
+    path_to_diffusers = get_path_to_diffusers_checkpoint(custom_weights, precision)
+    if next(Path(path_to_diffusers).iterdir(), None):
+        print("Checkpoint already loaded at : ", path_to_diffusers)
+        return path_to_diffusers
+    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")
+    num_in_channels = 9 if is_inpaint else 4
+    pipe = download_from_original_stable_diffusion_ckpt(
+        checkpoint_path_or_dict=custom_weights,
+        extract_ema=extract_ema,
+        from_safetensors=from_safetensors,
+        num_in_channels=num_in_channels,
+    )
+    if precision == "fp16":
+        pipe.to(dtype=torch.float16)
+    pipe.save_pretrained(path_to_diffusers)
+    del pipe
+    print("Loading complete")
+    return path_to_diffusers
+
+
+def save_irpa(weights_path, prepend_str):
+    weights = safetensors.torch.load_file(weights_path)
+    archive = ParameterArchiveBuilder()
+    for key in weights.keys():
+        new_key = prepend_str + key
+        archive.add_tensor(new_key, weights[key])
+
+    if "safetensors" in weights_path:
+        irpa_file = weights_path.replace(".safetensors", ".irpa")
+    elif "irpa" in weights_path:
+        irpa_file = weights_path
+    else:
+        return Exception(
+            "Invalid file format. Please provide a .safetensors or .irpa file."
+        )
+    archive.save(irpa_file)
+    return irpa_file
+
+
+def convert_original_vae(vae_checkpoint):
+    vae_state_dict = {}
+    for key in list(vae_checkpoint.keys()):
+        vae_state_dict["first_stage_model." + key] = vae_checkpoint.get(key)
+
+    config_url = (
+        "https://raw.githubusercontent.com/CompVis/stable-diffusion/"
+        "main/configs/stable-diffusion/v1-inference.yaml"
+    )
+    original_config_file = BytesIO(requests.get(config_url).content)
+    original_config = OmegaConf.load(original_config_file)
+    vae_config = create_vae_diffusers_config(original_config, image_size=512)
+
+    converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_state_dict, vae_config)
+    return converted_vae_checkpoint
+
+
+def process_custom_pipe_weights(custom_weights):
+    if custom_weights != "":
+        if custom_weights.startswith("https://civitai.com/api/"):
+            # download the checkpoint from civitai if we don't already have it
+            weights_path = get_civitai_checkpoint(custom_weights)
+
+            # act as if we were given the local file as custom_weights originally
+            custom_weights_tgt = get_path_to_diffusers_checkpoint(weights_path)
+            custom_weights_params = weights_path
+
+        else:
+            assert custom_weights.lower().endswith(
+                (".ckpt", ".safetensors")
+            ), "checkpoint files supported can be any of [.ckpt, .safetensors] type"
+            custom_weights_tgt = get_path_to_diffusers_checkpoint(custom_weights)
+            custom_weights_params = custom_weights
+
+        return custom_weights_params, custom_weights_tgt
+
+
+def get_civitai_checkpoint(url: str):
+    with requests.get(url, allow_redirects=True, stream=True) as response:
+        response.raise_for_status()
+
+        # civitai api returns the filename in the content disposition
+        base_filename = re.findall(
+            '"([^"]*)"', response.headers["Content-Disposition"]
+        )[0]
+        destination_path = Path.cwd() / (cmd_opts.model_dir or "models") / base_filename
+
+        # we don't have this model downloaded yet
+        if not destination_path.is_file():
+            print(f"downloading civitai model from {url} to {destination_path}")
+
+            size = int(response.headers["content-length"], 0)
+            progress_bar = tqdm(total=size, unit="iB", unit_scale=True)
+
+            with open(destination_path, "wb") as f:
+                for chunk in response.iter_content(chunk_size=65536):
+                    f.write(chunk)
+                    progress_bar.update(len(chunk))
+
+            progress_bar.close()
+
+        # we already have this model downloaded
+        else:
+            print(f"civitai model already downloaded to {destination_path}")
+
+        response.close()
+        return destination_path.as_posix()

apps/shark_studio/modules/embeddings.py

@@ -0,0 +1,185 @@
+import os
+import sys
+import torch
+import json
+import safetensors
+from dataclasses import dataclass
+from safetensors.torch import load_file
+from apps.shark_studio.web.utils.file_utils import (
+    get_checkpoint_pathfile,
+    get_path_stem,
+)
+
+
+@dataclass
+class LoRAweight:
+    up: torch.tensor
+    down: torch.tensor
+    mid: torch.tensor
+    alpha: torch.float32 = 1.0
+
+
+def processLoRA(model, use_lora, splitting_prefix, lora_strength=0.75):
+    state_dict = ""
+    if ".safetensors" in use_lora:
+        state_dict = load_file(use_lora)
+    else:
+        state_dict = torch.load(use_lora)
+
+    # gather the weights from the LoRA in a more convenient form, assumes
+    # everything will have an up.weight.
+    weight_dict: dict[str, LoRAweight] = {}
+    for key in state_dict:
+        if key.startswith(splitting_prefix) and key.endswith("up.weight"):
+            stem = key.split("up.weight")[0]
+            weight_key = stem.removesuffix(".lora_")
+            weight_key = weight_key.removesuffix("_lora_")
+            weight_key = weight_key.removesuffix(".lora_linear_layer.")
+
+            if weight_key not in weight_dict:
+                weight_dict[weight_key] = LoRAweight(
+                    state_dict[f"{stem}up.weight"],
+                    state_dict[f"{stem}down.weight"],
+                    state_dict.get(f"{stem}mid.weight", None),
+                    (
+                        state_dict[f"{weight_key}.alpha"]
+                        / state_dict[f"{stem}up.weight"].shape[1]
+                        if f"{weight_key}.alpha" in state_dict
+                        else 1.0
+                    ),
+                )
+
+    # Directly update weight in model
+
+    # Mostly adaptions of https://github.com/kohya-ss/sd-scripts/blob/main/networks/merge_lora.py
+    # and similar code in https://github.com/huggingface/diffusers/issues/3064
+
+    # TODO: handle mid weights (how do they even work?)
+    for key, lora_weight in weight_dict.items():
+        curr_layer = model
+        layer_infos = key.split(".")[0].split(splitting_prefix)[-1].split("_")
+
+        # find the target layer
+        temp_name = layer_infos.pop(0)
+        while len(layer_infos) > -1:
+            try:
+                curr_layer = curr_layer.__getattr__(temp_name)
+                if len(layer_infos) > 0:
+                    temp_name = layer_infos.pop(0)
+                elif len(layer_infos) == 0:
+                    break
+            except Exception:
+                if len(temp_name) > 0:
+                    temp_name += "_" + layer_infos.pop(0)
+                else:
+                    temp_name = layer_infos.pop(0)
+
+        weight = curr_layer.weight.data
+        scale = lora_weight.alpha * lora_strength
+        if len(weight.size()) == 2:
+            if len(lora_weight.up.shape) == 4:
+                weight_up = lora_weight.up.squeeze(3).squeeze(2).to(torch.float32)
+                weight_down = lora_weight.down.squeeze(3).squeeze(2).to(torch.float32)
+                change = torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
+            else:
+                change = torch.mm(lora_weight.up, lora_weight.down)
+        elif lora_weight.down.size()[2:4] == (1, 1):
+            weight_up = lora_weight.up.squeeze(3).squeeze(2).to(torch.float32)
+            weight_down = lora_weight.down.squeeze(3).squeeze(2).to(torch.float32)
+            change = torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
+        else:
+            change = torch.nn.functional.conv2d(
+                lora_weight.down.permute(1, 0, 2, 3),
+                lora_weight.up,
+            ).permute(1, 0, 2, 3)
+
+        curr_layer.weight.data += change * scale
+
+    return model
+
+
+def update_lora_weight_for_unet(unet, use_lora, lora_strength):
+    extensions = [".bin", ".safetensors", ".pt"]
+    if not any([extension in use_lora for extension in extensions]):
+        # We assume if it is a HF ID with standalone LoRA weights.
+        unet.load_attn_procs(use_lora)
+        return unet
+
+    main_file_name = get_path_stem(use_lora)
+    if ".bin" in use_lora:
+        main_file_name += ".bin"
+    elif ".safetensors" in use_lora:
+        main_file_name += ".safetensors"
+    elif ".pt" in use_lora:
+        main_file_name += ".pt"
+    else:
+        sys.exit("Only .bin and .safetensors format for LoRA is supported")
+
+    try:
+        dir_name = os.path.dirname(use_lora)
+        unet.load_attn_procs(dir_name, weight_name=main_file_name)
+        return unet
+    except:
+        return processLoRA(unet, use_lora, "lora_unet_", lora_strength)
+
+
+def update_lora_weight(model, use_lora, model_name, lora_strength=1.0):
+    if "unet" in model_name:
+        return update_lora_weight_for_unet(model, use_lora, lora_strength)
+    try:
+        return processLoRA(model, use_lora, "lora_te_", lora_strength)
+    except:
+        return None
+
+
+def get_lora_metadata(lora_filename):
+    # get the metadata from the file
+    filename = get_checkpoint_pathfile(lora_filename, "lora")
+    with safetensors.safe_open(filename, framework="pt", device="cpu") as f:
+        metadata = f.metadata()
+
+    # guard clause for if there isn't any metadata
+    if not metadata:
+        return None
+
+    # metadata is a dictionary of strings, the values of the keys we're
+    # interested in are actually json, and need to be loaded as such
+    tag_frequencies = json.loads(metadata.get("ss_tag_frequency", str("{}")))
+    dataset_dirs = json.loads(metadata.get("ss_dataset_dirs", str("{}")))
+    tag_dirs = [dir for dir in tag_frequencies.keys()]
+
+    # gather the tag frequency information for all the datasets trained
+    all_frequencies = {}
+    for dataset in tag_dirs:
+        frequencies = sorted(
+            [entry for entry in tag_frequencies[dataset].items()],
+            reverse=True,
+            key=lambda x: x[1],
+        )
+
+        # get a figure for the total number of images processed for this dataset
+        # either then number actually listed or in its dataset_dir entry or
+        # the highest frequency's number if that doesn't exist
+        img_count = dataset_dirs.get(dir, {}).get("img_count", frequencies[0][1])
+
+        # add the dataset frequencies to the overall frequencies replacing the
+        # frequency counts on the tags with a percentage/ratio
+        all_frequencies.update(
+            [(entry[0], entry[1] / img_count) for entry in frequencies]
+        )
+
+    trained_model_id = " ".join(
+        [
+            metadata.get("ss_sd_model_hash", ""),
+            metadata.get("ss_sd_model_name", ""),
+            metadata.get("ss_base_model_version", ""),
+        ]
+    ).strip()
+
+    # return the topmost <count> of all frequencies in all datasets
+    return {
+        "model": trained_model_id,
+        "frequencies": sorted(
+            all_frequencies.items(), reverse=True, key=lambda x: x[1]
+        ),
+    }

apps/shark_studio/modules/img_processing.py

@@ -0,0 +1,204 @@
+import os
+import re
+import json
+import torch
+import numpy as np
+
+from csv import DictWriter
+from PIL import Image, PngImagePlugin
+from pathlib import Path
+from datetime import datetime as dt
+from base64 import decode
+
+
+resamplers = {
+    "Lanczos": Image.Resampling.LANCZOS,
+    "Nearest Neighbor": Image.Resampling.NEAREST,
+    "Bilinear": Image.Resampling.BILINEAR,
+    "Bicubic": Image.Resampling.BICUBIC,
+    "Hamming": Image.Resampling.HAMMING,
+    "Box": Image.Resampling.BOX,
+}
+
+resampler_list = resamplers.keys()
+
+
+# save output images and the inputs corresponding to it.
+def save_output_img(output_img, img_seed, extra_info=None):
+    from apps.shark_studio.web.utils.file_utils import (
+        get_generated_imgs_path,
+        get_generated_imgs_todays_subdir,
+    )
+    from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+
+    if extra_info is None:
+        extra_info = {}
+    elif "progress" in extra_info.keys():
+        extra_info.pop("progress")
+    generated_imgs_path = Path(
+        get_generated_imgs_path(), get_generated_imgs_todays_subdir()
+    )
+    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]", "_", extra_info["prompt"][0][:15])
+    out_img_name = f"{dt.now().strftime('%H%M%S')}_{prompt_slice}_{img_seed}"
+
+    img_model = extra_info["base_model_id"]
+    if extra_info["custom_weights"] not in [None, "None"]:
+        img_model = Path(os.path.basename(extra_info["custom_weights"])).stem
+
+    img_vae = None
+    if extra_info["custom_vae"]:
+        img_vae = Path(os.path.basename(extra_info["custom_vae"])).stem
+
+    img_loras = None
+    if extra_info["embeddings"]:
+        img_lora = []
+        for i in extra_info["embeddings"]:
+            img_lora += Path(os.path.basename(cmd_opts.use_lora)).stem
+        img_loras = ", ".join(img_lora)
+
+    if cmd_opts.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 cmd_opts.write_metadata_to_png:
+            # Using a conditional expression caused problems, so setting a new
+            # variable for now.
+            # if cmd_opts.use_hiresfix:
+            #    png_size_text = (
+            #        f"{cmd_opts.hiresfix_width}x{cmd_opts.hiresfix_height}"
+            #    )
+            # else:
+            png_size_text = f"{extra_info['width']}x{extra_info['height']}"
+
+            pngInfo.add_text(
+                "parameters",
+                f"{extra_info['prompt'][0]}"
+                f"\nNegative prompt: {extra_info['negative_prompt'][0]}"
+                f"\nSteps: {extra_info['steps']},"
+                f"Sampler: {extra_info['scheduler']}, "
+                f"CFG scale: {extra_info['guidance_scale']}, "
+                f"Seed: {img_seed},"
+                f"Size: {png_size_text}, "
+                f"Model: {img_model}, "
+                f"VAE: {img_vae}, "
+                f"LoRA: {img_loras}",
+            )
+
+        output_img.save(out_img_path, "PNG", pnginfo=pngInfo)
+
+        if cmd_opts.output_img_format not in ["png", "jpg"]:
+            print(
+                f"[ERROR] Format {cmd_opts.output_img_format} is not "
+                f"supported yet. Image saved as png instead."
+                f"Supported formats: png / jpg"
+            )
+
+    # To be as low-impact as possible to the existing CSV format, we append
+    # "VAE" and "LORA" to the end. However, it does not fit the hierarchy of
+    # importance for each data point. Something to consider.
+    new_entry = {}
+
+    new_entry.update(extra_info)
+
+    csv_mode = "a" if os.path.isfile(csv_path) else "w"
+    with open(csv_path, csv_mode, encoding="utf-8") as csv_obj:
+        dictwriter_obj = DictWriter(csv_obj, fieldnames=list(new_entry.keys()))
+        if csv_mode == "w":
+            dictwriter_obj.writeheader()
+        dictwriter_obj.writerow(new_entry)
+        csv_obj.close()
+
+    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)
+
+
+# For stencil, the input image can be of any size, but we need to ensure that
+# it conforms with our model constraints :-
+#   Both width and height should be in the range of [128, 768] and multiple of 8.
+# This utility function performs the transformation on the input image while
+# also maintaining the aspect ratio before sending it to the stencil pipeline.
+def resize_stencil(image: Image.Image, width, height, resampler_type=None):
+    aspect_ratio = width / height
+    min_size = min(width, height)
+    if min_size < 128:
+        n_size = 128
+        if width == min_size:
+            width = n_size
+            height = n_size / aspect_ratio
+        else:
+            height = n_size
+            width = n_size * aspect_ratio
+    width = int(width)
+    height = int(height)
+    n_width = width // 8
+    n_height = height // 8
+    n_width *= 8
+    n_height *= 8
+
+    min_size = min(width, height)
+    if min_size > 768:
+        n_size = 768
+        if width == min_size:
+            height = n_size
+            width = n_size * aspect_ratio
+        else:
+            width = n_size
+            height = n_size / aspect_ratio
+    width = int(width)
+    height = int(height)
+    n_width = width // 8
+    n_height = height // 8
+    n_width *= 8
+    n_height *= 8
+    if resampler_type in resamplers:
+        resampler = resamplers[resampler_type]
+    else:
+        resampler = resamplers["Nearest Neighbor"]
+    new_image = image.resize((n_width, n_height), resampler=resampler)
+    return new_image, n_width, n_height
+
+
+def process_sd_init_image(self, sd_init_image, resample_type):
+    if isinstance(sd_init_image, list):
+        images = []
+        for img in sd_init_image:
+            img, _ = self.process_sd_init_image(img, resample_type)
+            images.append(img)
+            is_img2img = True
+            return images, is_img2img
+    if isinstance(sd_init_image, str):
+        if os.path.isfile(sd_init_image):
+            sd_init_image = Image.open(sd_init_image, mode="r").convert("RGB")
+            image, is_img2img = self.process_sd_init_image(sd_init_image, resample_type)
+        else:
+            image = None
+            is_img2img = False
+    elif isinstance(sd_init_image, Image.Image):
+        image = sd_init_image.convert("RGB")
+    elif sd_init_image:
+        image = sd_init_image["image"].convert("RGB")
+    else:
+        image = None
+        is_img2img = False
+    if image:
+        resample_type = (
+            resamplers[resample_type]
+            if resample_type in resampler_list
+            # Fallback to Lanczos
+            else Image.Resampling.LANCZOS
+        )
+        image = image.resize((self.width, self.height), resample=resample_type)
+        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(self.dtype)
+        image_arr = 2 * (image_arr - 0.5)
+        is_img2img = True
+        image = image_arr
+    return image, is_img2img

apps/shark_studio/modules/logger.py

@@ -0,0 +1,37 @@
+import sys
+
+
+class Logger:
+    def __init__(self, filename, filter=None):
+        self.terminal = sys.stdout
+        self.log = open(filename, "w")
+        self.filter = filter
+
+    def write(self, message):
+        for x in message.split("\n"):
+            if self.filter in x:
+                self.log.write(message)
+            else:
+                self.terminal.write(message)
+
+    def flush(self):
+        self.terminal.flush()
+        self.log.flush()
+
+    def isatty(self):
+        return False
+
+
+def logger_test(x):
+    print("[LOG] This is a test")
+    print(f"This is another test, without the filter")
+    return x
+
+
+def read_sd_logs():
+    sys.stdout.flush()
+    with open("shark_tmp/sd.log", "r") as f:
+        return f.read()
+
+
+sys.stdout = Logger("shark_tmp/sd.log", filter="[LOG]")

apps/shark_studio/modules/pipeline.py

@@ -0,0 +1,205 @@
+from shark.iree_utils.compile_utils import (
+    get_iree_compiled_module,
+    load_vmfb_using_mmap,
+    clean_device_info,
+    get_iree_target_triple,
+)
+from apps.shark_studio.web.utils.file_utils import (
+    get_checkpoints_path,
+    get_resource_path,
+)
+from apps.shark_studio.modules.shared_cmd_opts import (
+    cmd_opts,
+)
+from iree import runtime as ireert
+from pathlib import Path
+import gc
+import os
+
+
+class SharkPipelineBase:
+    # This class is a lightweight base for managing an
+    # inference API class. It should provide methods for:
+    # - compiling a set (model map) of torch IR modules
+    # - preparing weights for an inference job
+    # - loading weights for an inference job
+    # - utilites like benchmarks, tests
+
+    def __init__(
+        self,
+        model_map: dict,
+        base_model_id: str,
+        static_kwargs: dict,
+        device: str,
+        import_mlir: bool = True,
+    ):
+        self.model_map = model_map
+        self.pipe_map = {}
+        self.static_kwargs = static_kwargs
+        self.base_model_id = base_model_id
+        self.triple = get_iree_target_triple(device)
+        self.device, self.device_id = clean_device_info(device)
+        self.import_mlir = import_mlir
+        self.iree_module_dict = {}
+        self.tmp_dir = get_resource_path(cmd_opts.tmp_dir)
+        if not os.path.exists(self.tmp_dir):
+            os.mkdir(self.tmp_dir)
+        self.tempfiles = {}
+        self.pipe_vmfb_path = ""
+
+    def get_compiled_map(self, pipe_id, submodel="None", init_kwargs={}) -> None:
+        # First checks whether we have .vmfbs precompiled, then populates the map
+        # with the precompiled executables and fetches executables for the rest of the map.
+        # The weights aren't static here anymore so this function should be a part of pipeline
+        # initialization. As soon as you have a pipeline ID unique to your static torch IR parameters,
+        # and your model map is populated with any IR - unique model IDs and their static params,
+        # call this method to get the artifacts associated with your map.
+        self.pipe_id = self.safe_name(pipe_id)
+        self.pipe_vmfb_path = Path(os.path.join(get_checkpoints_path(), self.pipe_id))
+        self.pipe_vmfb_path.mkdir(parents=False, exist_ok=True)
+        if submodel == "None":
+            print("\n[LOG] Gathering any pre-compiled artifacts....")
+            for key in self.model_map:
+                self.get_compiled_map(pipe_id, submodel=key)
+        else:
+            self.pipe_map[submodel] = {}
+            self.get_precompiled(self.pipe_id, submodel)
+            ireec_flags = []
+            if submodel in self.iree_module_dict:
+                return
+            elif "vmfb_path" in self.pipe_map[submodel]:
+                return
+            elif submodel not in self.tempfiles:
+                print(
+                    f"\n[LOG] Tempfile for {submodel} not found. Fetching torch IR..."
+                )
+                if submodel in self.static_kwargs:
+                    init_kwargs = self.static_kwargs[submodel]
+                for key in self.static_kwargs["pipe"]:
+                    if key not in init_kwargs:
+                        init_kwargs[key] = self.static_kwargs["pipe"][key]
+                self.import_torch_ir(submodel, init_kwargs)
+                self.get_compiled_map(pipe_id, submodel)
+            else:
+                ireec_flags = (
+                    self.model_map[submodel]["ireec_flags"]
+                    if "ireec_flags" in self.model_map[submodel]
+                    else []
+                )
+
+                weights_path = self.get_io_params(submodel)
+                if weights_path:
+                    ireec_flags.append("--iree-opt-const-eval=False")
+
+                self.iree_module_dict[submodel] = get_iree_compiled_module(
+                    self.tempfiles[submodel],
+                    device=self.device,
+                    frontend="torch",
+                    mmap=True,
+                    external_weight_file=weights_path,
+                    extra_args=ireec_flags,
+                    write_to=os.path.join(self.pipe_vmfb_path, submodel + ".vmfb"),
+                )
+        return
+
+    def get_io_params(self, submodel):
+        if "external_weight_file" in self.static_kwargs[submodel]:
+            # we are using custom weights
+            weights_path = self.static_kwargs[submodel]["external_weight_file"]
+        elif "external_weight_path" in self.static_kwargs[submodel]:
+            # we are using the default weights for the HF model
+            weights_path = self.static_kwargs[submodel]["external_weight_path"]
+        else:
+            # assume the torch IR contains the weights.
+            weights_path = None
+        return weights_path
+
+    def get_precompiled(self, pipe_id, submodel="None"):
+        if submodel == "None":
+            for model in self.model_map:
+                self.get_precompiled(pipe_id, model)
+        vmfbs = []
+        for dirpath, dirnames, filenames in os.walk(self.pipe_vmfb_path):
+            vmfbs.extend(filenames)
+            break
+        for file in vmfbs:
+            if submodel in file:
+                self.pipe_map[submodel]["vmfb_path"] = os.path.join(
+                    self.pipe_vmfb_path, file
+                )
+        return
+
+    def import_torch_ir(self, submodel, kwargs):
+        torch_ir = self.model_map[submodel]["initializer"](
+            **self.safe_dict(kwargs), compile_to="torch"
+        )
+        if submodel == "clip":
+            # clip.export_clip_model returns (torch_ir, tokenizer)
+            torch_ir = torch_ir[0]
+
+        self.tempfiles[submodel] = os.path.join(
+            self.tmp_dir, f"{submodel}.torch.tempfile"
+        )
+
+        with open(self.tempfiles[submodel], "w+") as f:
+            f.write(torch_ir)
+        del torch_ir
+        gc.collect()
+        return
+
+    def load_submodels(self, submodels: list):
+        for submodel in submodels:
+            if submodel in self.iree_module_dict:
+                print(f"\n[LOG] {submodel} is ready for inference.")
+                continue
+            if "vmfb_path" in self.pipe_map[submodel]:
+                weights_path = self.get_io_params(submodel)
+                # print(
+                #     f"\n[LOG] Loading .vmfb for {submodel} from {self.pipe_map[submodel]['vmfb_path']}"
+                # )
+                self.iree_module_dict[submodel] = {}
+                (
+                    self.iree_module_dict[submodel]["vmfb"],
+                    self.iree_module_dict[submodel]["config"],
+                    self.iree_module_dict[submodel]["temp_file_to_unlink"],
+                ) = load_vmfb_using_mmap(
+                    self.pipe_map[submodel]["vmfb_path"],
+                    self.device,
+                    device_idx=0,
+                    rt_flags=[],
+                    external_weight_file=weights_path,
+                )
+            else:
+                self.get_compiled_map(self.pipe_id, submodel)
+        return
+
+    def unload_submodels(self, submodels: list):
+        for submodel in submodels:
+            if submodel in self.iree_module_dict:
+                del self.iree_module_dict[submodel]
+                gc.collect()
+        return
+
+    def run(self, submodel, inputs):
+        if not isinstance(inputs, list):
+            inputs = [inputs]
+        inp = [
+            ireert.asdevicearray(
+                self.iree_module_dict[submodel]["config"].device, input
+            )
+            for input in inputs
+        ]
+        return self.iree_module_dict[submodel]["vmfb"]["main"](*inp)
+
+    def safe_name(self, name):
+        return name.replace("/", "_").replace("-", "_").replace("\\", "_")
+
+    def safe_dict(self, kwargs: dict):
+        flat_args = {}
+        for i in kwargs:
+            if isinstance(kwargs[i], dict) and "pass_dict" not in kwargs[i]:
+                flat_args[i] = [kwargs[i][j] for j in kwargs[i]]
+            else:
+                flat_args[i] = kwargs[i]
+
+        return flat_args

apps/shark_studio/modules/prompt_encoding.py

@@ -0,0 +1,376 @@
+from typing import List, Optional, Union
+from iree import runtime as ireert
+import re
+import torch
+import numpy as np
+
+re_attention = re.compile(
+    r"""
+\\\(|
+\\\)|
+\\\[|
+\\]|
+\\\\|
+\\|
+\(|
+\[|
+:([+-]?[.\d]+)\)|
+\)|
+]|
+[^\\()\[\]:]+|
+:
+""",
+    re.X,
+)
+
+
+def parse_prompt_attention(text):
+    """
+    Parses a string with attention tokens and returns a list of pairs:
+        text and its associated weight.
+    Accepted tokens are:
+      (abc) - increases attention to abc by a multiplier of 1.1
+      (abc:3.12) - increases attention to abc by a multiplier of 3.12
+      [abc] - decreases attention to abc by a multiplier of 1.1
+      \( - literal character '('
+      \[ - literal character '['
+      \) - literal character ')'
+      \] - literal character ']'
+      \\ - literal character '\'
+      anything else - just text
+    >>> parse_prompt_attention('normal text')
+    [['normal text', 1.0]]
+    >>> parse_prompt_attention('an (important) word')
+    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+    >>> parse_prompt_attention('(unbalanced')
+    [['unbalanced', 1.1]]
+    >>> parse_prompt_attention('\(literal\]')
+    [['(literal]', 1.0]]
+    >>> parse_prompt_attention('(unnecessary)(parens)')
+    [['unnecessaryparens', 1.1]]
+    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+    [['a ', 1.0],
+     ['house', 1.5730000000000004],
+     [' ', 1.1],
+     ['on', 1.0],
+     [' a ', 1.1],
+     ['hill', 0.55],
+     [', sun, ', 1.1],
+     ['sky', 1.4641000000000006],
+     ['.', 1.1]]
+    """
+
+    res = []
+    round_brackets = []
+    square_brackets = []
+
+    round_bracket_multiplier = 1.1
+    square_bracket_multiplier = 1 / 1.1
+
+    def multiply_range(start_position, multiplier):
+        for p in range(start_position, len(res)):
+            res[p][1] *= multiplier
+
+    for m in re_attention.finditer(text):
+        text = m.group(0)
+        weight = m.group(1)
+
+        if text.startswith("\\"):
+            res.append([text[1:], 1.0])
+        elif text == "(":
+            round_brackets.append(len(res))
+        elif text == "[":
+            square_brackets.append(len(res))
+        elif weight is not None and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), float(weight))
+        elif text == ")" and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), round_bracket_multiplier)
+        elif text == "]" and len(square_brackets) > 0:
+            multiply_range(square_brackets.pop(), square_bracket_multiplier)
+        else:
+            res.append([text, 1.0])
+
+    for pos in round_brackets:
+        multiply_range(pos, round_bracket_multiplier)
+
+    for pos in square_brackets:
+        multiply_range(pos, square_bracket_multiplier)
+
+    if len(res) == 0:
+        res = [["", 1.0]]
+
+    # merge runs of identical weights
+    i = 0
+    while i + 1 < len(res):
+        if res[i][1] == res[i + 1][1]:
+            res[i][0] += res[i + 1][0]
+            res.pop(i + 1)
+        else:
+            i += 1
+
+    return res
+
+
+def get_prompts_with_weights(pipe, prompt: List[str], max_length: int):
+    r"""
+    Tokenize a list of prompts and return its tokens with weights of each token.
+    No padding, starting or ending token is included.
+    """
+    tokens = []
+    weights = []
+    truncated = False
+    for text in prompt:
+        texts_and_weights = parse_prompt_attention(text)
+        text_token = []
+        text_weight = []
+        for word, weight in texts_and_weights:
+            # tokenize and discard the starting and the ending token
+            token = pipe.tokenizer(word).input_ids[1:-1]
+            text_token += token
+            # copy the weight by length of token
+            text_weight += [weight] * len(token)
+            # stop if the text is too long (longer than truncation limit)
+            if len(text_token) > max_length:
+                truncated = True
+                break
+        # truncate
+        if len(text_token) > max_length:
+            truncated = True
+            text_token = text_token[:max_length]
+            text_weight = text_weight[:max_length]
+        tokens.append(text_token)
+        weights.append(text_weight)
+    if truncated:
+        print(
+            "Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples"
+        )
+    return tokens, weights
+
+
+def pad_tokens_and_weights(
+    tokens,
+    weights,
+    max_length,
+    bos,
+    eos,
+    no_boseos_middle=True,
+    chunk_length=77,
+):
+    r"""
+    Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
+    """
+    max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
+    weights_length = (
+        max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
+    )
+    for i in range(len(tokens)):
+        tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i]))
+        if no_boseos_middle:
+            weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
+        else:
+            w = []
+            if len(weights[i]) == 0:
+                w = [1.0] * weights_length
+            else:
+                for j in range(max_embeddings_multiples):
+                    w.append(1.0)  # weight for starting token in this chunk
+                    w += weights[i][
+                        j
+                        * (chunk_length - 2) : min(
+                            len(weights[i]), (j + 1) * (chunk_length - 2)
+                        )
+                    ]
+                    w.append(1.0)  # weight for ending token in this chunk
+                w += [1.0] * (weights_length - len(w))
+            weights[i] = w[:]
+
+    return tokens, weights
+
+
+def get_unweighted_text_embeddings(
+    pipe,
+    text_input,
+    chunk_length: int,
+    no_boseos_middle: Optional[bool] = True,
+):
+    """
+    When the length of tokens is a multiple of the capacity of the text encoder,
+    it should be split into chunks and sent to the text encoder individually.
+    """
+    max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
+    if max_embeddings_multiples > 1:
+        text_embeddings = []
+        for i in range(max_embeddings_multiples):
+            # extract the i-th chunk
+            text_input_chunk = text_input[
+                :, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2
+            ].clone()
+
+            # cover the head and the tail by the starting and the ending tokens
+            text_input_chunk[:, 0] = text_input[0, 0]
+            text_input_chunk[:, -1] = text_input[0, -1]
+
+            text_embedding = pipe.run("clip", text_input_chunk)[0].to_host()
+
+            if no_boseos_middle:
+                if i == 0:
+                    # discard the ending token
+                    text_embedding = text_embedding[:, :-1]
+                elif i == max_embeddings_multiples - 1:
+                    # discard the starting token
+                    text_embedding = text_embedding[:, 1:]
+                else:
+                    # discard both starting and ending tokens
+                    text_embedding = text_embedding[:, 1:-1]
+
+            text_embeddings.append(text_embedding)
+        # SHARK: Convert the result to tensor
+        # text_embeddings = torch.concat(text_embeddings, axis=1)
+        text_embeddings_np = np.concatenate(np.array(text_embeddings))
+        text_embeddings = torch.from_numpy(text_embeddings_np)
+    else:
+        text_embeddings = pipe.run("clip", text_input)[0]
+        text_embeddings = torch.from_numpy(text_embeddings.to_host())
+    return text_embeddings
+
+
+# This function deals with NoneType values occuring in tokens after padding
+# It switches out None with 49407 as truncating None values causes matrix dimension errors,
+def filter_nonetype_tokens(tokens: List[List]):
+    return [[49407 if token is None else token for token in tokens[0]]]
+
+
+def get_weighted_text_embeddings(
+    pipe,
+    prompt: List[str],
+    uncond_prompt: List[str] = None,
+    max_embeddings_multiples: Optional[int] = 8,
+    no_boseos_middle: Optional[bool] = True,
+    skip_parsing: Optional[bool] = False,
+    skip_weighting: Optional[bool] = False,
+):
+    max_length = (pipe.model_max_length - 2) * max_embeddings_multiples + 2
+
+    if not skip_parsing:
+        prompt_tokens, prompt_weights = get_prompts_with_weights(
+            pipe, prompt, max_length - 2
+        )
+        if uncond_prompt is not None:
+            uncond_tokens, uncond_weights = get_prompts_with_weights(
+                pipe, uncond_prompt, max_length - 2
+            )
+    else:
+        prompt_tokens = [
+            token[1:-1]
+            for token in pipe.tokenizer(
+                prompt, max_length=max_length, truncation=True
+            ).input_ids
+        ]
+        prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
+        if uncond_prompt is not None:
+            if isinstance(uncond_prompt, str):
+                uncond_prompt = [uncond_prompt]
+            uncond_tokens = [
+                token[1:-1]
+                for token in pipe.tokenizer(
+                    uncond_prompt, max_length=max_length, truncation=True
+                ).input_ids
+            ]
+            uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
+
+    # round up the longest length of tokens to a multiple of (model_max_length - 2)
+    max_length = max([len(token) for token in prompt_tokens])
+    if uncond_prompt is not None:
+        max_length = max(max_length, max([len(token) for token in uncond_tokens]))
+    max_embeddings_multiples = min(
+        max_embeddings_multiples,
+        (max_length - 1) // (pipe.model_max_length - 2) + 1,
+    )
+    max_embeddings_multiples = max(1, max_embeddings_multiples)
+
+    max_length = (pipe.model_max_length - 2) * max_embeddings_multiples + 2
+
+    # pad the length of tokens and weights
+    bos = pipe.tokenizer.bos_token_id
+    eos = pipe.tokenizer.eos_token_id
+    prompt_tokens, prompt_weights = pad_tokens_and_weights(
+        prompt_tokens,
+        prompt_weights,
+        max_length,
+        bos,
+        eos,
+        no_boseos_middle=no_boseos_middle,
+        chunk_length=pipe.model_max_length,
+    )
+
+    # FIXME: This is a hacky fix caused by tokenizer padding with None values
+    prompt_tokens = filter_nonetype_tokens(prompt_tokens)
+
+    # prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)
+    prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device="cpu")
+    if uncond_prompt is not None:
+        uncond_tokens, uncond_weights = pad_tokens_and_weights(
+            uncond_tokens,
+            uncond_weights,
+            max_length,
+            bos,
+            eos,
+            no_boseos_middle=no_boseos_middle,
+            chunk_length=pipe.model_max_length,
+        )
+
+        # FIXME: This is a hacky fix caused by tokenizer padding with None values
+        uncond_tokens = filter_nonetype_tokens(uncond_tokens)
+
+        # uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)
+        uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device="cpu")
+
+    # get the embeddings
+    text_embeddings = get_unweighted_text_embeddings(
+        pipe,
+        prompt_tokens,
+        pipe.model_max_length,
+        no_boseos_middle=no_boseos_middle,
+    )
+    # prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.device)
+    prompt_weights = torch.tensor(prompt_weights, dtype=torch.float, device="cpu")
+    if uncond_prompt is not None:
+        uncond_embeddings = get_unweighted_text_embeddings(
+            pipe,
+            uncond_tokens,
+            pipe.model_max_length,
+            no_boseos_middle=no_boseos_middle,
+        )
+        # uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.device)
+        uncond_weights = torch.tensor(uncond_weights, dtype=torch.float, device="cpu")
+
+    # assign weights to the prompts and normalize in the sense of mean
+    # TODO: should we normalize by chunk or in a whole (current implementation)?
+    if (not skip_parsing) and (not skip_weighting):
+        previous_mean = (
+            text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
+        )
+        text_embeddings *= prompt_weights.unsqueeze(-1)
+        current_mean = (
+            text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
+        )
+        text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
+        if uncond_prompt is not None:
+            previous_mean = (
+                uncond_embeddings.float()
+                .mean(axis=[-2, -1])
+                .to(uncond_embeddings.dtype)
+            )
+            uncond_embeddings *= uncond_weights.unsqueeze(-1)
+            current_mean = (
+                uncond_embeddings.float()
+                .mean(axis=[-2, -1])
+                .to(uncond_embeddings.dtype)
+            )
+            uncond_embeddings *= (
+                (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
+            )
+
+    if uncond_prompt is not None:
+        return text_embeddings, uncond_embeddings
+    return text_embeddings, None

apps/shark_studio/modules/schedulers.py

@@ -0,0 +1,118 @@
+# from shark_turbine.turbine_models.schedulers import export_scheduler_model
+from diffusers import (
+    LCMScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    DDPMScheduler,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    KDPM2DiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DEISMultistepScheduler,
+    DPMSolverSinglestepScheduler,
+    KDPM2AncestralDiscreteScheduler,
+    HeunDiscreteScheduler,
+)
+
+
+def get_schedulers(model_id):
+    # TODO: switch over to turbine and run all on GPU
+    print(f"\n[LOG] Initializing schedulers from model id: {model_id}")
+    schedulers = dict()
+    schedulers["PNDM"] = PNDMScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    # schedulers["DDPM"] = DDPMScheduler.from_pretrained(
+    #     model_id,
+    #     subfolder="scheduler",
+    # )
+    # schedulers["KDPM2Discrete"] = KDPM2DiscreteScheduler.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["LCMScheduler"] = LCMScheduler.from_pretrained(
+    #     model_id,
+    #     subfolder="scheduler",
+    # )
+    # schedulers["DPMSolverMultistep"] = DPMSolverMultistepScheduler.from_pretrained(
+    #     model_id, subfolder="scheduler", algorithm_type="dpmsolver"
+    # )
+    # schedulers["DPMSolverMultistep++"] = DPMSolverMultistepScheduler.from_pretrained(
+    #     model_id, subfolder="scheduler", algorithm_type="dpmsolver++"
+    # )
+    # schedulers["DPMSolverMultistepKarras"] = (
+    #     DPMSolverMultistepScheduler.from_pretrained(
+    #         model_id,
+    #         subfolder="scheduler",
+    #         use_karras_sigmas=True,
+    #     )
+    # )
+    # schedulers["DPMSolverMultistepKarras++"] = (
+    #     DPMSolverMultistepScheduler.from_pretrained(
+    #         model_id,
+    #         subfolder="scheduler",
+    #         algorithm_type="dpmsolver++",
+    #         use_karras_sigmas=True,
+    #     )
+    # )
+    schedulers["EulerDiscrete"] = EulerDiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["EulerAncestralDiscrete"] = (
+        EulerAncestralDiscreteScheduler.from_pretrained(
+            model_id,
+            subfolder="scheduler",
+        )
+    )
+    # schedulers["DEISMultistep"] = DEISMultistepScheduler.from_pretrained(
+    #     model_id,
+    #     subfolder="scheduler",
+    # )
+    # schedulers["DPMSolverSinglestep"] = DPMSolverSinglestepScheduler.from_pretrained(
+    #     model_id,
+    #     subfolder="scheduler",
+    # )
+    # schedulers["KDPM2AncestralDiscrete"] = (
+    #     KDPM2AncestralDiscreteScheduler.from_pretrained(
+    #         model_id,
+    #         subfolder="scheduler",
+    #     )
+    # )
+    # schedulers["HeunDiscrete"] = HeunDiscreteScheduler.from_pretrained(
+    #     model_id,
+    #     subfolder="scheduler",
+    # )
+    return schedulers
+
+
+def export_scheduler_model(model):
+    return "None", "None"
+
+
+scheduler_model_map = {
+    # "PNDM": export_scheduler_model("PNDMScheduler"),
+    # "DPMSolverSDE": export_scheduler_model("DpmSolverSDEScheduler"),
+    "EulerDiscrete": export_scheduler_model("EulerDiscreteScheduler"),
+    "EulerAncestralDiscrete": export_scheduler_model("EulerAncestralDiscreteScheduler"),
+    # "LCM": export_scheduler_model("LCMScheduler"),
+    # "LMSDiscrete": export_scheduler_model("LMSDiscreteScheduler"),
+    # "DDPM": export_scheduler_model("DDPMScheduler"),
+    # "DDIM": export_scheduler_model("DDIMScheduler"),
+    # "DPMSolverMultistep": export_scheduler_model("DPMSolverMultistepScheduler"),
+    # "KDPM2Discrete": export_scheduler_model("KDPM2DiscreteScheduler"),
+    # "DEISMultistep": export_scheduler_model("DEISMultistepScheduler"),
+    # "DPMSolverSinglestep": export_scheduler_model("DPMSolverSingleStepScheduler"),
+    # "KDPM2AncestralDiscrete": export_scheduler_model("KDPM2AncestralDiscreteScheduler"),
+    # "HeunDiscrete": export_scheduler_model("HeunDiscreteScheduler"),
+}

apps/shark_studio/modules/seed.py

@@ -0,0 +1,66 @@
+import numpy as np
+import json
+from random import (
+    randint,
+    seed as seed_random,
+    getstate as random_getstate,
+    setstate as random_setstate,
+)
+
+
+# Generate and return a new seed if the provided one is not in the
+# supported range (including -1)
+def sanitize_seed(seed: int | str):
+    seed = int(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
+
+
+# take a seed expression in an input format and convert it to
+# a list of integers, where possible
+def parse_seed_input(seed_input: str | list | int):
+    if isinstance(seed_input, str):
+        try:
+            seed_input = json.loads(seed_input)
+        except (ValueError, TypeError):
+            seed_input = None
+
+    if isinstance(seed_input, int):
+        return [seed_input]
+
+    if isinstance(seed_input, list) and all(type(seed) is int for seed in seed_input):
+        return seed_input
+
+    raise TypeError(
+        "Seed input must be an integer or an array of integers in JSON format"
+    )
+
+
+# Generate a set of seeds from an input expression for batch_count batches,
+# optionally using that input as the rng seed for any randomly generated seeds.
+def batch_seeds(seed_input: str | list | int, batch_count: int, repeatable=False):
+    # turn the input into a list if possible
+    seeds = parse_seed_input(seed_input)
+
+    # slice or pad the list to be of batch_count length
+    seeds = seeds[:batch_count] + [-1] * (batch_count - len(seeds))
+
+    if repeatable:
+        if all(seed < 0 for seed in seeds):
+            seeds[0] = sanitize_seed(seeds[0])
+
+        # set seed for the rng based on what we have so far
+        saved_random_state = random_getstate()
+        seed_random(str([n for n in seeds if n > -1]))
+
+    # generate any seeds that are unspecified
+    seeds = [sanitize_seed(seed) for seed in seeds]
+
+    if repeatable:
+        # reset the rng back to normal
+        random_setstate(saved_random_state)
+
+    return seeds

apps/shark_studio/modules/shared_cmd_opts.py

@@ -0,0 +1,793 @@
+import argparse
+import os
+from pathlib import Path
+
+from apps.shark_studio.modules.img_processing import resampler_list
+
+
+def path_expand(s):
+    return Path(s).expanduser().resolve()
+
+
+def is_valid_file(arg):
+    if not os.path.exists(arg):
+        return None
+    else:
+        return arg
+
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+##############################################################################
+# Stable Diffusion Params
+##############################################################################
+p.add_argument(
+    "-a",
+    "--app",
+    default="txt2img",
+    help="Which app to use, one of: txt2img, img2img, outpaint, inpaint.",
+)
+p.add_argument(
+    "-p",
+    "--prompt",
+    nargs="+",
+    default=[
+        "A hi-res photo of a red street racer drifting around a curve on a mountain, high altitude, at night, tokyo in the background, 8k"
+    ],
+    help="Text of which images to be generated.",
+)
+
+p.add_argument(
+    "--negative_prompt",
+    nargs="+",
+    default=[
+        "watermark, signature, logo, text, lowres, ((monochrome, grayscale)), "
+        "blurry, ugly, blur, oversaturated, cropped"
+    ],
+    help="Text you don't want to see in the generated image.",
+)
+
+p.add_argument(
+    "--sd_init_image",
+    type=str,
+    help="Path to the image input for img2img/inpainting.",
+)
+
+p.add_argument(
+    "--steps",
+    type=int,
+    default=2,
+    help="The number of steps to do the sampling.",
+)
+
+p.add_argument(
+    "--seed",
+    type=str,
+    default=-1,
+    help="The seed or list of seeds 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,
+    choices=range(128, 1025, 8),
+    help="The height of the output image.",
+)
+
+p.add_argument(
+    "--width",
+    type=int,
+    default=512,
+    choices=range(128, 1025, 8),
+    help="The width of the output image.",
+)
+
+p.add_argument(
+    "--guidance_scale",
+    type=float,
+    default=0,
+    help="The value to be used for guidance scaling.",
+)
+
+p.add_argument(
+    "--noise_level",
+    type=int,
+    default=20,
+    help="The value to be used for noise level of upscaler.",
+)
+
+p.add_argument(
+    "--max_length",
+    type=int,
+    default=64,
+    help="Max length of the tokenizer output, options are 64 and 77.",
+)
+
+p.add_argument(
+    "--max_embeddings_multiples",
+    type=int,
+    default=5,
+    help="The max multiple length of prompt embeddings compared to the max "
+    "output length of text encoder.",
+)
+
+p.add_argument(
+    "--strength",
+    type=float,
+    default=0.8,
+    help="The strength of change applied on the given input image for " "img2img.",
+)
+
+p.add_argument(
+    "--use_hiresfix",
+    type=bool,
+    default=False,
+    help="Use Hires Fix to do higher resolution images, while trying to "
+    "avoid the issues that come with it. This is accomplished by first "
+    "generating an image using txt2img, then running it through img2img.",
+)
+
+p.add_argument(
+    "--hiresfix_height",
+    type=int,
+    default=768,
+    choices=range(128, 769, 8),
+    help="The height of the Hires Fix image.",
+)
+
+p.add_argument(
+    "--hiresfix_width",
+    type=int,
+    default=768,
+    choices=range(128, 769, 8),
+    help="The width of the Hires Fix image.",
+)
+
+p.add_argument(
+    "--hiresfix_strength",
+    type=float,
+    default=0.6,
+    help="The denoising strength to apply for the Hires Fix.",
+)
+
+p.add_argument(
+    "--resample_type",
+    type=str,
+    default="Nearest Neighbor",
+    choices=resampler_list,
+    help="The resample type to use when resizing an image before being run "
+    "through stable diffusion.",
+)
+
+##############################################################################
+# Stable Diffusion Training Params
+##############################################################################
+
+p.add_argument(
+    "--lora_save_dir",
+    type=str,
+    default="models/lora/",
+    help="Directory to save the lora fine tuned model.",
+)
+
+p.add_argument(
+    "--training_images_dir",
+    type=str,
+    default="models/lora/training_images/",
+    help="Directory containing images that are an example of the prompt.",
+)
+
+p.add_argument(
+    "--training_steps",
+    type=int,
+    default=2000,
+    help="The number of steps to train.",
+)
+
+##############################################################################
+# Inpainting and Outpainting Params
+##############################################################################
+
+p.add_argument(
+    "--mask_path",
+    type=str,
+    help="Path to the mask image input for inpainting.",
+)
+
+p.add_argument(
+    "--inpaint_full_res",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If inpaint only masked area or whole picture.",
+)
+
+p.add_argument(
+    "--inpaint_full_res_padding",
+    type=int,
+    default=32,
+    choices=range(0, 257, 4),
+    help="Number of pixels for only masked padding.",
+)
+
+p.add_argument(
+    "--pixels",
+    type=int,
+    default=128,
+    choices=range(8, 257, 8),
+    help="Number of expended pixels for one direction for outpainting.",
+)
+
+p.add_argument(
+    "--mask_blur",
+    type=int,
+    default=8,
+    choices=range(0, 65),
+    help="Number of blur pixels for outpainting.",
+)
+
+p.add_argument(
+    "--left",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If extend left for outpainting.",
+)
+
+p.add_argument(
+    "--right",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If extend right for outpainting.",
+)
+
+p.add_argument(
+    "--up",
+    "--top",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If extend top for outpainting.",
+)
+
+p.add_argument(
+    "--down",
+    "--bottom",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If extend bottom for outpainting.",
+)
+
+p.add_argument(
+    "--noise_q",
+    type=float,
+    default=1.0,
+    help="Fall-off exponent for outpainting (lower=higher detail) "
+    "(min=0.0, max=4.0).",
+)
+
+p.add_argument(
+    "--color_variation",
+    type=float,
+    default=0.05,
+    help="Color variation for outpainting (min=0.0, max=1.0).",
+)
+
+##############################################################################
+# 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=True,
+    action=argparse.BooleanOptionalAction,
+    help="Imports the model from torch module to shark_module otherwise "
+    "downloads the model from shark_tank.",
+)
+
+p.add_argument(
+    "--use_tuned",
+    default=False,
+    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="DDIM",
+    help="Other supported schedulers are [DDIM, PNDM, LMSDiscrete, "
+    "DPMSolverMultistep, DPMSolverMultistep++, DPMSolverMultistepKarras, "
+    "DPMSolverMultistepKarras++, EulerDiscrete, EulerAncestralDiscrete, "
+    "DEISMultistep, KDPM2AncestralDiscrete, DPMSolverSinglestep, DDPM, "
+    "HeunDiscrete].",
+)
+
+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=os.path.join(os.getcwd(), "generated_imgs"),
+    help="Directory path to save the output images and json.",
+)
+
+p.add_argument(
+    "--batch_count",
+    type=int,
+    default=1,
+    help="Number of batches to be generated with random seeds in " "single execution.",
+)
+
+p.add_argument(
+    "--repeatable_seeds",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="The seed of the first batch will be used as the rng seed to "
+    "generate the subsequent seeds for subsequent batches in that run.",
+)
+
+p.add_argument(
+    "--custom_weights",
+    type=str,
+    default="",
+    help="Path to a .safetensors or .ckpt file for SD pipeline weights.",
+)
+
+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(
+    "--base_model_id",
+    type=str,
+    default="stabilityai/stable-diffusion-2-1-base",
+    help="The repo-id of hugging face.",
+)
+
+p.add_argument(
+    "--low_cpu_mem_usage",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Use the accelerate package to reduce cpu memory consumption.",
+)
+
+p.add_argument(
+    "--attention_slicing",
+    type=str,
+    default="none",
+    help="Amount of attention slicing to use (one of 'max', 'auto', 'none', "
+    "or an integer).",
+)
+
+p.add_argument(
+    "--use_stencil",
+    choices=["canny", "openpose", "scribble", "zoedepth"],
+    help="Enable the stencil feature.",
+)
+
+p.add_argument(
+    "--control_mode",
+    choices=["Prompt", "Balanced", "Controlnet"],
+    default="Balanced",
+    help="How Controlnet injection should be prioritized.",
+)
+
+p.add_argument(
+    "--use_lora",
+    type=str,
+    default="",
+    help="Use standalone LoRA weight using a HF ID or a checkpoint " "file (~3 MB).",
+)
+
+p.add_argument(
+    "--use_quantize",
+    type=str,
+    default="none",
+    help="Runs the quantized version of stable diffusion model. "
+    "This is currently in experimental phase. "
+    "Currently, only runs the stable-diffusion-2-1-base model in "
+    "int8 quantization.",
+)
+
+p.add_argument(
+    "--lowvram",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Load and unload models for low VRAM.",
+)
+
+p.add_argument(
+    "--hf_auth_token",
+    type=str,
+    default=None,
+    help="Specify your own huggingface authentication tokens for models like Llama2.",
+)
+
+p.add_argument(
+    "--external_weights",
+    type=str,
+    default=None,
+    help="What type of externalized weights to use. Currently options are 'safetensors' and defaults to inlined weights.",
+)
+
+p.add_argument(
+    "--device_allocator_heap_key",
+    type=str,
+    default="",
+    help="Specify heap key for device caching allocator."
+    "Expected form: max_allocation_size;max_allocation_capacity;max_free_allocation_count"
+    "Example: --device_allocator_heap_key='*;1gib' (will limit caching on device to 1 gigabyte)",
+)
+
+##############################################################################
+# IREE - Vulkan supported flags
+##############################################################################
+
+p.add_argument(
+    "--iree_vulkan_target_triple",
+    type=str,
+    default="",
+    help="Specify target triple for vulkan.",
+)
+
+p.add_argument(
+    "--iree_metal_target_platform",
+    type=str,
+    default="",
+    help="Specify target triple for metal.",
+)
+
+##############################################################################
+# 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 benchmark 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.",
+)
+
+p.add_argument(
+    "--import_debug",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If import_mlir is True, saves mlir via the debug option "
+    "in shark importer. Does nothing if import_mlir is false (the default).",
+)
+
+p.add_argument(
+    "--compile_debug",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Flag to toggle debug assert/verify flags for imported IR in the"
+    "iree-compiler. Default to false.",
+)
+
+p.add_argument(
+    "--iree_constant_folding",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="Controls constant folding in iree-compile for all SD models.",
+)
+
+p.add_argument(
+    "--data_tiling",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Controls data tiling in iree-compile for all SD models.",
+)
+
+p.add_argument(
+    "--quantization",
+    type=str,
+    default="None",
+    help="Quantization to be used for api-exposed model.",
+)
+
+##############################################################################
+# Web UI flags
+##############################################################################
+p.add_argument(
+    "--defaults",
+    default="sdxl-turbo.json",
+    type=str,
+    help="Path to the default API request .json file. Works for CLI and webui.",
+)
+
+p.add_argument(
+    "--webui",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="controls whether the webui is launched.",
+)
+
+p.add_argument(
+    "--progress_bar",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="Flag for removing the progress bar animation during " "image generation.",
+)
+
+p.add_argument(
+    "--tmp_dir",
+    type=str,
+    default=os.path.join(os.getcwd(), "shark_tmp"),
+    help="Path to tmp directory",
+)
+
+p.add_argument(
+    "--config_dir",
+    type=str,
+    default=os.path.join(os.getcwd(), "configs"),
+    help="Path to config directory",
+)
+
+p.add_argument(
+    "--model_dir",
+    type=str,
+    default=os.path.join(os.getcwd(), "models"),
+    help="Path to directory where all .ckpts are stored in order to populate "
+    "them in the web UI.",
+)
+
+# TODO: replace API flag when these can be run together
+p.add_argument(
+    "--ui",
+    type=str,
+    default="app" if os.name == "nt" else "web",
+    help="One of: [api, app, web].",
+)
+
+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.",
+)
+
+p.add_argument(
+    "--api",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Flag for enabling rest API.",
+)
+
+p.add_argument(
+    "--api_accept_origin",
+    action="append",
+    type=str,
+    help="An origin to be accepted by the REST api for Cross Origin"
+    "Resource Sharing (CORS). Use multiple times for multiple origins, "
+    'or use --api_accept_origin="*" to accept all origins. If no origins '
+    "are set no CORS headers will be returned by the api. Use, for "
+    "instance, if you need to access the REST api from Javascript running "
+    "in a web browser.",
+)
+
+p.add_argument(
+    "--debug",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Flag for enabling debugging log in WebUI.",
+)
+
+p.add_argument(
+    "--output_gallery",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="Flag for removing the output gallery tab, and avoid exposing "
+    "images under --output_dir in the UI.",
+)
+
+p.add_argument(
+    "--configs_path",
+    default=None,
+    type=str,
+    help="Path to .json config directory.",
+)
+
+p.add_argument(
+    "--output_gallery_followlinks",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Flag for whether the output gallery tab in the UI should "
+    "follow symlinks when listing subdirectories under --output_dir.",
+)
+
+p.add_argument(
+    "--api_log",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Enables Compatibility API logging.",
+)
+
+##############################################################################
+# 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.",
+)
+##############################################################################
+# SD model auto-tuner flags
+##############################################################################
+
+p.add_argument(
+    "--tuned_config_dir",
+    type=path_expand,
+    default="./",
+    help="Directory to save the tuned config file.",
+)
+
+p.add_argument(
+    "--num_iters",
+    type=int,
+    default=400,
+    help="Number of iterations for tuning.",
+)
+
+p.add_argument(
+    "--search_op",
+    type=str,
+    default="all",
+    help="Op to be optimized, options are matmul, bmm, conv and all.",
+)
+
+##############################################################################
+# DocuChat Flags
+##############################################################################
+
+p.add_argument(
+    "--run_docuchat_web",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Specifies whether the docuchat's web version is running or not.",
+)
+
+##############################################################################
+# rocm Flags
+##############################################################################
+
+p.add_argument(
+    "--iree_rocm_target_chip",
+    type=str,
+    default="",
+    help="Add the rocm device architecture ex gfx1100, gfx90a, etc. Use `hipinfo` "
+    "or `iree-run-module --dump_devices=rocm` or `hipinfo` to get desired arch name",
+)
+
+cmd_opts, unknown = p.parse_known_args()
+if cmd_opts.import_debug:
+    os.environ["IREE_SAVE_TEMPS"] = os.path.join(
+        os.getcwd(), cmd_opts.hf_model_id.replace("/", "_")
+    )

apps/shark_studio/modules/timer.py

@@ -0,0 +1,106 @@
+import time
+import argparse
+
+
+class TimerSubcategory:
+    def __init__(self, timer, category):
+        self.timer = timer
+        self.category = category
+        self.start = None
+        self.original_base_category = timer.base_category
+
+    def __enter__(self):
+        self.start = time.time()
+        self.timer.base_category = self.original_base_category + self.category + "/"
+        self.timer.subcategory_level += 1
+
+        if self.timer.print_log:
+            print(f"{'  ' * self.timer.subcategory_level}{self.category}:")
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        elapsed_for_subcategroy = time.time() - self.start
+        self.timer.base_category = self.original_base_category
+        self.timer.add_time_to_record(
+            self.original_base_category + self.category,
+            elapsed_for_subcategroy,
+        )
+        self.timer.subcategory_level -= 1
+        self.timer.record(self.category, disable_log=True)
+
+
+class Timer:
+    def __init__(self, print_log=False):
+        self.start = time.time()
+        self.records = {}
+        self.total = 0
+        self.base_category = ""
+        self.print_log = print_log
+        self.subcategory_level = 0
+
+    def elapsed(self):
+        end = time.time()
+        res = end - self.start
+        self.start = end
+        return res
+
+    def add_time_to_record(self, category, amount):
+        if category not in self.records:
+            self.records[category] = 0
+
+        self.records[category] += amount
+
+    def record(self, category, extra_time=0, disable_log=False):
+        e = self.elapsed()
+
+        self.add_time_to_record(self.base_category + category, e + extra_time)
+
+        self.total += e + extra_time
+
+        if self.print_log and not disable_log:
+            print(
+                f"{'  ' * self.subcategory_level}{category}: done in {e + extra_time:.3f}s"
+            )
+
+    def subcategory(self, name):
+        self.elapsed()
+
+        subcat = TimerSubcategory(self, name)
+        return subcat
+
+    def summary(self):
+        res = f"{self.total:.1f}s"
+
+        additions = [
+            (category, time_taken)
+            for category, time_taken in self.records.items()
+            if time_taken >= 0.1 and "/" not in category
+        ]
+        if not additions:
+            return res
+
+        res += " ("
+        res += ", ".join(
+            [f"{category}: {time_taken:.1f}s" for category, time_taken in additions]
+        )
+        res += ")"
+
+        return res
+
+    def dump(self):
+        return {"total": self.total, "records": self.records}
+
+    def reset(self):
+        self.__init__()
+
+
+parser = argparse.ArgumentParser(add_help=False)
+parser.add_argument(
+    "--log-startup",
+    action="store_true",
+    help="print a detailed log of what's happening at startup",
+)
+args = parser.parse_known_args()[0]
+
+startup_timer = Timer(print_log=args.log_startup)
+
+startup_record = None

apps/shark_studio/shark_studio.spec

@@ -0,0 +1,48 @@
+# -*- mode: python ; coding: utf-8 -*-
+from apps.shark_studio.studio_imports import pathex, datas, hiddenimports
+
+binaries = []
+
+block_cipher = None
+
+a = Analysis(
+    ['web/index.py'],
+    pathex=pathex,
+    binaries=binaries,
+    datas=datas,
+    hiddenimports=hiddenimports,
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+    noarchive=False,
+    module_collection_mode={
+        'gradio': 'py',  # Collect gradio package as source .py files
+    },
+)
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    [],
+    name='nodai_shark_studio',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=False,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)

apps/shark_studio/shark_studio_apionly.spec

@@ -0,0 +1,45 @@
+# -*- mode: python ; coding: utf-8 -*-
+from apps.shark_studio.studio_imports_apionly import pathex, datas, hiddenimports
+
+binaries = []
+
+block_cipher = None
+
+a = Analysis(
+    ['web/index.py'],
+    pathex=pathex,
+    binaries=binaries,
+    datas=datas,
+    hiddenimports=hiddenimports,
+    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_sd3_server',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=False,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)

apps/shark_studio/studio_imports.py

@@ -0,0 +1,62 @@
+from PyInstaller.utils.hooks import collect_data_files
+from PyInstaller.utils.hooks import copy_metadata
+from PyInstaller.utils.hooks import collect_submodules
+
+import sys
+
+sys.setrecursionlimit(sys.getrecursionlimit() * 5)
+
+# python path for pyinstaller
+pathex = [
+    ".",
+]
+
+# datafiles for pyinstaller
+datas = []
+datas += copy_metadata("torch")
+datas += copy_metadata("tokenizers")
+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("importlib_metadata")
+datas += copy_metadata("safetensors")
+datas += copy_metadata("Pillow")
+datas += copy_metadata("sentencepiece")
+datas += copy_metadata("pyyaml")
+datas += copy_metadata("huggingface-hub")
+datas += copy_metadata("gradio")
+datas += collect_data_files("torch")
+datas += collect_data_files("tokenizers")
+datas += collect_data_files("diffusers")
+datas += collect_data_files("transformers")
+datas += collect_data_files("gradio")
+datas += collect_data_files("gradio_client")
+datas += collect_data_files("iree", include_py_files=True)
+datas += collect_data_files("shark-turbine", include_py_files=True)
+datas += collect_data_files("tqdm")
+datas += collect_data_files("sentencepiece")
+datas += collect_data_files("jsonschema")
+datas += collect_data_files("jsonschema_specifications")
+datas += collect_data_files("cpuinfo")
+datas += [
+    ("web/ui/css/*", "ui/css"),
+    ("web/ui/js/*", "ui/js"),
+    ("web/ui/logos/*", "logos"),
+]
+
+
+# hidden imports for pyinstaller
+hiddenimports = ["apps", "shark-turbine"]
+hiddenimports += [x for x in collect_submodules("gradio") if "tests" not in x]
+hiddenimports += [x for x in collect_submodules("diffusers") if "tests" not in x]
+blacklist = ["tests", "convert"]
+hiddenimports += [
+    x
+    for x in collect_submodules("transformers")
+    if not any(kw in x for kw in blacklist)
+]
+hiddenimports += [x for x in collect_submodules("iree") if "test" not in x]
+hiddenimports += ["iree._runtime"]

apps/shark_studio/studio_imports_apionly.py

@@ -0,0 +1,46 @@
+from PyInstaller.utils.hooks import collect_data_files
+from PyInstaller.utils.hooks import copy_metadata
+from PyInstaller.utils.hooks import collect_submodules
+
+import sys
+
+sys.setrecursionlimit(sys.getrecursionlimit() * 5)
+
+# python path for pyinstaller
+pathex = [
+    ".",
+]
+
+# datafiles for pyinstaller
+datas = []
+datas += copy_metadata("torch")
+datas += copy_metadata("tokenizers")
+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("importlib_metadata")
+datas += copy_metadata("safetensors")
+datas += copy_metadata("Pillow")
+datas += copy_metadata("sentencepiece")
+datas += copy_metadata("pyyaml")
+datas += copy_metadata("huggingface-hub")
+datas += copy_metadata("gradio")
+datas += collect_data_files("torch")
+datas += collect_data_files("tokenizers")
+datas += collect_data_files("diffusers")
+datas += collect_data_files("transformers")
+datas += collect_data_files("iree", include_py_files=True)
+datas += collect_data_files("tqdm")
+datas += collect_data_files("jsonschema")
+datas += collect_data_files("jsonschema_specifications")
+datas += collect_data_files("cpuinfo")
+
+
+# hidden imports for pyinstaller
+hiddenimports = ["apps", "shark-turbine"]
+hiddenimports += [x for x in collect_submodules("diffusers") if "tests" not in x]
+hiddenimports += [x for x in collect_submodules("iree") if "test" not in x]
+hiddenimports += ["iree._runtime"]

apps/shark_studio/tests/api_test.py

@@ -0,0 +1,58 @@
+# Copyright 2023 Nod Labs, Inc
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import logging
+import unittest
+import json
+import gc
+from apps.shark_studio.api.llm import LanguageModel, llm_chat_api
+from apps.shark_studio.api.sd import shark_sd_fn_dict_input, view_json_file
+from apps.shark_studio.web.utils.file_utils import get_resource_path
+
+# class SDAPITest(unittest.TestCase):
+#     def testSDSimple(self):
+#         from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+#         import apps.shark_studio.web.utils.globals as global_obj
+
+#         global_obj._init()
+
+#         sd_json = view_json_file(get_resource_path("../configs/default_sd_config.json"))
+#         sd_kwargs = json.loads(sd_json)
+#         for arg in vars(cmd_opts):
+#             if arg in sd_kwargs:
+#                 sd_kwargs[arg] = getattr(cmd_opts, arg)
+#         for i in shark_sd_fn_dict_input(sd_kwargs):
+#             print(i)
+
+
+class LLMAPITest(unittest.TestCase):
+    def test01_LLMSmall(self):
+        lm = LanguageModel(
+            "TinyPixel/small-llama2",
+            hf_auth_token=None,
+            device="cpu",
+            precision="fp32",
+            quantization="None",
+            streaming_llm=True,
+        )
+        count = 0
+        label = "Turkishoure Turkish"
+        for msg, _ in lm.chat("hi, what are you?"):
+            # skip first token output
+            if count == 0:
+                count += 1
+                continue
+            assert (
+                msg.strip(" ") == label
+            ), f"LLM API failed to return correct response, expected '{label}', received {msg}"
+            break
+        del lm
+        gc.collect()
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.DEBUG)
+    unittest.main()

apps/shark_studio/tests/export_unet.py

@@ -0,0 +1,41 @@
+import torch
+from diffusers import (
+    UNet2DConditionModel,
+)
+from torch.fx.experimental.proxy_tensor import make_fx
+
+
+class UnetModel(torch.nn.Module):
+    def __init__(self, hf_model_name):
+        super().__init__()
+        self.unet = UNet2DConditionModel.from_pretrained(
+            hf_model_name,
+            subfolder="unet",
+        )
+
+    def forward(self, sample, timestep, encoder_hidden_states, guidance_scale):
+        samples = torch.cat([sample] * 2)
+        unet_out = self.unet.forward(
+            samples, timestep, encoder_hidden_states, 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
+
+
+if __name__ == "__main__":
+    hf_model_name = "CompVis/stable-diffusion-v1-4"
+    unet = UnetModel(hf_model_name)
+    inputs = (torch.randn(1, 4, 64, 64), 1, torch.randn(2, 77, 768), 7.5)
+
+    fx_g = make_fx(
+        unet,
+        decomposition_table={},
+        tracing_mode="symbolic",
+        _allow_non_fake_inputs=True,
+        _allow_fake_constant=False,
+    )(*inputs)
+
+    print(fx_g)

apps/shark_studio/tests/rest_api_test.py

@@ -0,0 +1,45 @@
+import requests
+from PIL import Image
+import base64
+from io import BytesIO
+import json
+
+
+def llm_chat_test(verbose=False):
+    # Define values here
+    prompt = "What is the significance of the number 42?"
+
+    url = "http://127.0.0.1:8080/v1/chat/completions"
+
+    headers = {
+        "User-Agent": "PythonTest",
+        "Accept": "*/*",
+        "Accept-Encoding": "gzip, deflate, br",
+    }
+
+    data = {
+        "model": "Trelis/Llama-2-7b-chat-hf-function-calling-v2",
+        "messages": [
+            {
+                "role": "",
+                "content": prompt,
+            }
+        ],
+        "device": "vulkan://0",
+        "max_tokens": 4096,
+    }
+
+    res = requests.post(url=url, json=data, headers=headers, timeout=1000)
+    res_dict = json.loads(res.content.decode("utf-8"))
+    print(f"[chat] response from server was : {res.status_code} {res.reason}")
+
+    if verbose or res.status_code != 200:
+        print(f"\n{res_dict['choices'][0]['message']['content']}\n")
+
+
+if __name__ == "__main__":
+    # "Exercises the chatbot REST API of Shark. Make sure "
+    # "Shark is running in API mode on 127.0.0.1:8080 before running"
+    # "this script."
+
+    llm_chat_test(verbose=True)

apps/shark_studio/tools/params_prefixer.py

@@ -0,0 +1,20 @@
+from apps.shark_studio.modules.ckpt_processing import save_irpa
+import argparse
+import safetensors
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--input",
+    type=str,
+    default="",
+    help="input safetensors/irpa",
+)
+parser.add_argument(
+    "--prefix",
+    type=str,
+    default="",
+    help="prefix to add to all the keys in the irpa",
+)
+args = parser.parse_args()
+output_file = save_irpa(args.input, args.prefix)
+print("saved irpa to", output_file, "with prefix", args.prefix)

apps/shark_studio/web/api/compat.py

@@ -0,0 +1,220 @@
+import base64
+import io
+import os
+import time
+import datetime
+import uvicorn
+import ipaddress
+import requests
+import threading
+import collections
+import gradio as gr
+from PIL import Image, PngImagePlugin
+from threading import Lock
+from io import BytesIO
+from fastapi import APIRouter, Depends, FastAPI, Request, Response
+from fastapi.security import HTTPBasic, HTTPBasicCredentials
+from fastapi.exceptions import HTTPException
+from fastapi.responses import JSONResponse
+from fastapi.encoders import jsonable_encoder
+
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+
+
+def decode_base64_to_image(encoding):
+    if encoding.startswith("http://") or encoding.startswith("https://"):
+        headers = {}
+        response = requests.get(encoding, timeout=30, headers=headers)
+        try:
+            image = Image.open(BytesIO(response.content))
+            return image
+        except Exception as e:
+            raise HTTPException(status_code=500, detail="Invalid image url") from e
+
+    if encoding.startswith("data:image/"):
+        encoding = encoding.split(";")[1].split(",")[1]
+    try:
+        image = Image.open(BytesIO(base64.b64decode(encoding)))
+        return image
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="Invalid encoded image") from e
+
+
+def encode_pil_to_base64(image):
+    with io.BytesIO() as output_bytes:
+        use_metadata = False
+        metadata = PngImagePlugin.PngInfo()
+        for key, value in image.info.items():
+            if isinstance(key, str) and isinstance(value, str):
+                metadata.add_text(key, value)
+                use_metadata = True
+        image.save(
+            output_bytes,
+            format="PNG",
+            pnginfo=(metadata if use_metadata else None),
+        )
+
+        bytes_data = output_bytes.getvalue()
+
+    return base64.b64encode(bytes_data)
+
+
+# reference: https://gist.github.com/vitaliyp/6d54dd76ca2c3cdfc1149d33007dc34a
+class FIFOLock(object):
+    def __init__(self):
+        self._lock = threading.Lock()
+        self._inner_lock = threading.Lock()
+        self._pending_threads = collections.deque()
+
+    def acquire(self, blocking=True):
+        with self._inner_lock:
+            lock_acquired = self._lock.acquire(False)
+            if lock_acquired:
+                return True
+            elif not blocking:
+                return False
+
+            release_event = threading.Event()
+            self._pending_threads.append(release_event)
+
+        release_event.wait()
+        return self._lock.acquire()
+
+    def release(self):
+        with self._inner_lock:
+            if self._pending_threads:
+                release_event = self._pending_threads.popleft()
+                release_event.set()
+
+            self._lock.release()
+
+    __enter__ = acquire
+
+    def __exit__(self, t, v, tb):
+        self.release()
+
+
+def api_middleware(app: FastAPI):
+    rich_available = False
+    try:
+        if os.environ.get("WEBUI_RICH_EXCEPTIONS", None) is not None:
+            import anyio  # importing just so it can be placed on silent list
+            import starlette  # importing just so it can be placed on silent list
+            from rich.console import Console
+
+            console = Console()
+            rich_available = True
+    except Exception:
+        pass
+
+    @app.middleware("http")
+    async def log_and_time(req: Request, call_next):
+        ts = time.time()
+        res: Response = await call_next(req)
+        duration = str(round(time.time() - ts, 4))
+        res.headers["X-Process-Time"] = duration
+        endpoint = req.scope.get("path", "err")
+        if cmd_opts.api_log and endpoint.startswith("/sdapi"):
+            print(
+                "API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}".format(
+                    t=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
+                    code=res.status_code,
+                    ver=req.scope.get("http_version", "0.0"),
+                    cli=req.scope.get("client", ("0:0.0.0", 0))[0],
+                    prot=req.scope.get("scheme", "err"),
+                    method=req.scope.get("method", "err"),
+                    endpoint=endpoint,
+                    duration=duration,
+                )
+            )
+        return res
+
+    def handle_exception(request: Request, e: Exception):
+        err = {
+            "error": type(e).__name__,
+            "detail": vars(e).get("detail", ""),
+            "body": vars(e).get("body", ""),
+            "errors": str(e),
+        }
+        if not isinstance(
+            e, HTTPException
+        ):  # do not print backtrace on known httpexceptions
+            message = f"API error: {request.method}: {request.url} {err}"
+            if rich_available:
+                print(message)
+                console.print_exception(
+                    show_locals=True,
+                    max_frames=2,
+                    extra_lines=1,
+                    suppress=[anyio, starlette],
+                    word_wrap=False,
+                    width=min([console.width, 200]),
+                )
+            else:
+                print(message)
+                raise (e)
+        return JSONResponse(
+            status_code=vars(e).get("status_code", 500),
+            content=jsonable_encoder(err),
+        )
+
+    @app.middleware("http")
+    async def exception_handling(request: Request, call_next):
+        try:
+            return await call_next(request)
+        except Exception as e:
+            return handle_exception(request, e)
+
+    @app.exception_handler(Exception)
+    async def fastapi_exception_handler(request: Request, e: Exception):
+        return handle_exception(request, e)
+
+    @app.exception_handler(HTTPException)
+    async def http_exception_handler(request: Request, e: HTTPException):
+        return handle_exception(request, e)
+
+
+class ApiCompat:
+    def __init__(self, app: FastAPI, queue_lock: Lock):
+        self.router = APIRouter()
+        self.app = app
+        self.queue_lock = queue_lock
+        api_middleware(self.app)
+
+        # self.add_api_route("/sdapi/v1/txt2img", shark_sd_api, methods=["POST"])
+
+        self.default_script_arg_txt2img = []
+        self.default_script_arg_img2img = []
+
+    def add_api_route(self, path: str, endpoint, **kwargs):
+        return self.app.add_api_route(path, endpoint, **kwargs)
+
+    def launch(self, server_name, port, root_path):
+        self.app.include_router(self.router)
+        uvicorn.run(
+            self.app,
+            host=server_name,
+            port=port,
+            root_path=root_path,
+        )
+
+    # def kill_studio(self):
+    #     restart.stop_program()
+
+    # def restart_studio(self):
+    #     if restart.is_restartable():
+    #         restart.restart_program()
+    #     return Response(status_code=501)
+
+    # def preprocess(self, args: dict):
+    #     try:
+    #         studio.state.begin(job="preprocess")
+    #         preprocess(**args)
+    #         studio.state.end()
+    #         return models.PreprocessResponse(info="preprocess complete")
+    #     except:
+    #         studio.state.end()
+
+    # def stop_studio(request):
+    #     studio.state.server_command = "stop"
+    #     return Response("Stopping.")

apps/shark_studio/web/api/sd.py

@@ -0,0 +1,115 @@
+import base64
+
+from fastapi import FastAPI
+
+from io import BytesIO
+from PIL import Image
+from pydantic import BaseModel, Field
+from fastapi.exceptions import HTTPException
+
+from apps.shark_studio.api.sd import shark_sd_fn
+
+sdapi = FastAPI()
+
+
+class GenerationInputData(BaseModel):
+    prompt: list = [""]
+    negative_prompt: list = [""]
+    hf_model_id: str | None = None
+    height: int = Field(default=512, ge=128, le=1024, multiple_of=8)
+    width: int = Field(default=512, ge=128, le=1024, multiple_of=8)
+    sampler_name: str = "EulerDiscrete"
+    cfg_scale: float = Field(default=7.5, ge=1)
+    steps: int = Field(default=20, ge=1, le=100)
+    seed: int = Field(default=-1)
+    n_iter: int = Field(default=1)
+    config: dict = None
+
+
+class GenerationResponseData(BaseModel):
+    images: list[str] = Field(description="Generated images, Base64 encoded")
+    properties: dict = {}
+    info: str
+
+
+def encode_pil_to_base64(images: list[Image.Image]):
+    encoded_imgs = []
+    for image in images:
+        with BytesIO() as output_bytes:
+            image.save(output_bytes, format="PNG")
+            bytes_data = output_bytes.getvalue()
+            encoded_imgs.append(base64.b64encode(bytes_data))
+    return encoded_imgs
+
+
+def decode_base64_to_image(encoding: str):
+    if encoding.startswith("data:image/"):
+        encoding = encoding.split(";", 1)[1].split(",", 1)[1]
+    try:
+        image = Image.open(BytesIO(base64.b64decode(encoding)))
+        return image
+    except Exception as err:
+        print(err)
+        raise HTTPException(status_code=400, detail="Invalid encoded image")
+
+
+@sdapi.post(
+    "/v1/txt2img",
+    summary="Does text to image generation",
+    response_model=GenerationResponseData,
+)
+def txt2img_api(InputData: GenerationInputData):
+    model_id = (
+        InputData.hf_model_id or "stabilityai/stable-diffusion-3-medium-diffusers"
+    )
+    scheduler = "FlowEulerDiscrete"
+    print(
+        f"Prompt: {InputData.prompt}, "
+        f"Negative Prompt: {InputData.negative_prompt}, "
+        f"Seed: {InputData.seed},"
+        f"Model: {model_id}, "
+        f"Scheduler: {scheduler}. "
+    )
+    if not getattr(InputData, "config"):
+        InputData.config = {
+            "precision": "fp16",
+            "device": "rocm",
+            "target_triple": "gfx1150",
+        }
+
+    res = shark_sd_fn(
+        InputData.prompt,
+        InputData.negative_prompt,
+        None,
+        InputData.height,
+        InputData.width,
+        InputData.steps,
+        None,
+        InputData.cfg_scale,
+        InputData.seed,
+        custom_vae=None,
+        batch_count=InputData.n_iter,
+        batch_size=1,
+        scheduler=scheduler,
+        base_model_id=model_id,
+        custom_weights=None,
+        precision=InputData.config["precision"],
+        device=InputData.config["device"],
+        target_triple=InputData.config["target_triple"],
+        output_type="pil",
+        ondemand=False,
+        compiled_pipeline=False,
+        resample_type=None,
+        controlnets=[],
+        embeddings=[],
+    )
+
+    # Since we're not streaming we just want the last generator result
+    for items_so_far in res:
+        items = items_so_far
+
+    return {
+        "images": encode_pil_to_base64(items[0]),
+        "parameters": {},
+        "info": items[1],
+    }

apps/shark_studio/web/index.py

@@ -0,0 +1,226 @@
+from multiprocessing import Process, freeze_support
+
+freeze_support()
+from PIL import Image
+
+import os
+import time
+import sys
+import logging
+import apps.shark_studio.api.initializers as initialize
+
+
+from apps.shark_studio.modules import timer
+
+startup_timer = timer.startup_timer
+startup_timer.record("launcher")
+
+initialize.imports()
+
+if sys.platform == "darwin":
+    os.environ["DYLD_LIBRARY_PATH"] = "/usr/local/lib"
+    # import before IREE to avoid MLIR library issues
+    import torch_mlir
+
+
+def create_api(app):
+    from apps.shark_studio.web.api.compat import ApiCompat, FIFOLock
+
+    queue_lock = FIFOLock()
+    api = ApiCompat(app, queue_lock)
+    return api
+
+
+def api_only():
+    from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+    from apps.shark_studio.web.api.sd import sdapi
+    from fastapi import FastAPI
+
+    initialize.initialize()
+
+    app = FastAPI()
+    initialize.setup_middleware(app)
+    app.mount("/sdapi/", sdapi)
+    api = create_api(app)
+
+    # from modules import script_callbacks
+    # script_callbacks.before_ui_callback()
+    # script_callbacks.app_started_callback(None, app)
+
+    print(f"Startup time: {startup_timer.summary()}.")
+    api.launch(
+        server_name="0.0.0.0",
+        port=cmd_opts.server_port,
+        root_path="",
+    )
+
+
+def launch_webui(address):
+    from tkinter import Tk
+    import webview
+    import gradio as gr
+
+    window = Tk()
+
+    # get screen width and height of display and make it more reasonably
+    # sized as we aren't making it full-screen or maximized
+    width = int(window.winfo_screenwidth() * 0.81)
+    height = int(window.winfo_screenheight() * 0.91)
+    webview.create_window(
+        "SHARK AI Studio",
+        url=address,
+        width=width,
+        height=height,
+        text_select=True,
+    )
+    webview.start(private_mode=False, storage_path=os.getcwd())
+
+
+def webui():
+    from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+    from apps.shark_studio.web.ui.utils import (
+        amdicon_loc,
+        amdlogo_loc,
+    )
+
+    launch_api = cmd_opts.api
+    initialize.initialize()
+
+    # from ui.chat import chat_element
+    from ui.sd import sd_element
+    from ui.outputgallery import outputgallery_element
+
+    # required to do multiprocessing in a pyinstaller freeze
+    freeze_support()
+
+    # if args.api or "api" in args.ui.split(","):
+    #     from apps.shark_studio.api.llm import (
+    #         chat,
+    #     )
+    #     from apps.shark_studio.web.api import sdapi
+    #
+    #     from fastapi import FastAPI, APIRouter
+    #     from fastapi.middleware.cors import CORSMiddleware
+    #     import uvicorn
+    #
+    #     # init global sd pipeline and config
+    #     global_obj._init()
+    #
+    #     api = FastAPI()
+    #     api.mount("/sdapi/", sdapi)
+    #
+    #     # chat APIs needed for compatibility with multiple extensions using OpenAI API
+    #     api.add_api_route(
+    #         "/v1/chat/completions", llm_chat_api, methods=["post"]
+    #     )
+    #     api.add_api_route("/v1/completions", llm_chat_api, methods=["post"])
+    #     api.add_api_route("/chat/completions", llm_chat_api, methods=["post"])
+    #     api.add_api_route("/completions", llm_chat_api, methods=["post"])
+    #     api.add_api_route(
+    #         "/v1/engines/codegen/completions", llm_chat_api, methods=["post"]
+    #     )
+    #     api.include_router(APIRouter())
+    #
+    #     # deal with CORS requests if CORS accept origins are set
+    #     if args.api_accept_origin:
+    #         print(
+    #             f"API Configured for CORS. Accepting origins: { args.api_accept_origin }"
+    #         )
+    #         api.add_middleware(
+    #             CORSMiddleware,
+    #             allow_origins=args.api_accept_origin,
+    #             allow_methods=["GET", "POST"],
+    #             allow_headers=["*"],
+    #         )
+    #     else:
+    #         print("API not configured for CORS")
+    #
+    #     uvicorn.run(api, host="0.0.0.0", port=args.server_port)
+    #     sys.exit(0)
+    import gradio as gr
+
+    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")
+    gradio_workarounds = resource_path("ui/js/sd_gradio_workarounds.js")
+
+    # from apps.shark_studio.web.ui import load_ui_from_script
+
+    def register_button_click(button, selectedid, inputs, outputs):
+        button.click(
+            lambda x: (
+                x[0]["name"] if len(x) != 0 else None,
+                gr.Tabs.update(selected=selectedid),
+            ),
+            inputs,
+            outputs,
+        )
+
+    def register_outputgallery_button(button, selectedid, inputs, outputs):
+        button.click(
+            lambda x: (
+                x,
+                gr.Tabs.update(selected=selectedid),
+            ),
+            inputs,
+            outputs,
+        )
+
+    with gr.Blocks(
+        css=dark_theme,
+        js=gradio_workarounds,
+        analytics_enabled=False,
+        title="Shark Studio 2.0",
+    ) as studio_web:
+        amd_logo = Image.open(amdlogo_loc)
+        gr.Image(
+            value=amd_logo,
+            show_label=False,
+            interactive=False,
+            elem_id="tab_bar_logo",
+            show_download_button=False,
+        )
+        with gr.Tabs() as tabs:
+            # NOTE: If adding, removing, or re-ordering tabs, make sure that they
+            # have a unique id that doesn't clash with any of the other tabs,
+            # and that the order in the code here is the order they should
+            # appear in the ui, as the id value doesn't determine the order.
+
+            # Where possible, avoid changing the id of any tab that is the
+            # destination of one of the 'send to' buttons. If you do have to change
+            # that id, make sure you update the relevant register_button_click calls
+            # further down with the new id.
+            with gr.TabItem(label="Stable Diffusion", id=0):
+                sd_element.render()
+            with gr.TabItem(label="Output Gallery", id=1):
+                outputgallery_element.render()
+            # with gr.TabItem(label="Chat Bot", id=2):
+            #     chat_element.render()
+
+    studio_web.queue()
+
+    # if args.ui == "app":
+    #    t = Process(
+    #        target=launch_app, args=[f"http://localhost:{args.server_port}"]
+    #    )
+    #    t.start()
+    studio_web.launch(
+        share=cmd_opts.share,
+        inbrowser=True,
+        server_name="0.0.0.0",
+        server_port=cmd_opts.server_port,
+        favicon_path=amdicon_loc,
+    )
+
+
+if __name__ == "__main__":
+    from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+
+    api_only()
+    # if cmd_opts.webui == False:
+    #     api_only()
+    # else:
+    #     webui()

apps/shark_studio/web/ui/chat.py

@@ -0,0 +1,239 @@
+import gradio as gr
+import time
+import os
+from pathlib import Path
+from datetime import datetime as dt
+import json
+import sys
+from apps.shark_studio.api.llm import (
+    llm_model_map,
+    LanguageModel,
+)
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+import apps.shark_studio.web.utils.globals as global_obj
+
+B_SYS, E_SYS = "<s>", "</s>"
+
+B_SYS, E_SYS = "<s>", "</s>"
+
+B_SYS, E_SYS = "<s>", "</s>"
+
+
+def user(message, history):
+    # Append the user's message to the conversation history
+    return "", history + [[message, ""]]
+
+
+def append_bot_prompt(history, input_prompt):
+    user_prompt = f"{input_prompt} {E_SYS} {E_SYS}"
+    history += user_prompt
+    return history
+
+
+language_model = None
+
+
+def get_default_config():
+    return False
+
+
+# model_vmfb_key = ""
+
+
+def chat_fn(
+    prompt_prefix,
+    history,
+    model,
+    device,
+    precision,
+    download_vmfb,
+    config_file,
+    streaming_llm,
+    cli=False,
+):
+    global language_model
+    if streaming_llm and prompt_prefix == "Clear":
+        language_model = None
+        return "Clearing history...", ""
+    if language_model is None:
+        history[-1][-1] = "Getting the model ready..."
+        yield history, ""
+        language_model = LanguageModel(
+            model,
+            device=device,
+            precision=precision,
+            external_weights="safetensors",
+            use_system_prompt=prompt_prefix,
+            streaming_llm=streaming_llm,
+            hf_auth_token=cmd_opts.hf_auth_token,
+        )
+        history[-1][-1] = "Getting the model ready... Done"
+        yield history, ""
+        history[-1][-1] = ""
+    token_count = 0
+    total_time = 0.001  # In order to avoid divide by zero error
+    prefill_time = 0
+    is_first = True
+    for text, exec_time in language_model.chat(history):
+        history[-1][-1] = f"{text}{E_SYS}"
+        if is_first:
+            prefill_time = exec_time
+            is_first = False
+            yield history, f"Prefill: {prefill_time:.2f}"
+        else:
+            total_time += exec_time
+            token_count += 1
+            tokens_per_sec = token_count / total_time
+            yield history, f"Prefill: {prefill_time:.2f} seconds\n Decode: {tokens_per_sec:.2f} tokens/sec"
+
+
+def view_json_file(file_obj):
+    content = ""
+    with open(file_obj.name, "r") as fopen:
+        content = fopen.read()
+    return content
+
+
+with gr.Blocks(title="Chat") as chat_element:
+    with gr.Row():
+        model_choices = list(llm_model_map.keys())
+        model = gr.Dropdown(
+            label="Select Model",
+            value=model_choices[0],
+            choices=model_choices,
+            allow_custom_value=True,
+        )
+        supported_devices = global_obj.get_device_list()
+        enabled = True
+        if len(supported_devices) == 0:
+            supported_devices = ["cpu-task"]
+        supported_devices = [x for x in supported_devices if "sync" not in x]
+        device = gr.Dropdown(
+            label="Device",
+            value=supported_devices[0],
+            choices=supported_devices,
+            interactive=enabled,
+            allow_custom_value=True,
+        )
+        precision = gr.Radio(
+            label="Precision",
+            value="fp32",
+            choices=[
+                # "int4",
+                # "int8",
+                # "fp16",
+                "fp32",
+            ],
+            visible=False,
+        )
+        tokens_time = gr.Textbox(label="Tokens generated per second")
+        with gr.Column():
+            download_vmfb = gr.Checkbox(
+                label="Download vmfb from Shark tank if available",
+                value=False,
+                interactive=True,
+                visible=False,
+            )
+            streaming_llm = gr.Checkbox(
+                label="Run in streaming mode (requires recompilation)",
+                value=True,
+                interactive=False,
+                visible=False,
+            )
+            prompt_prefix = gr.Checkbox(
+                label="Add System Prompt",
+                value=True,
+                interactive=True,
+            )
+
+    chatbot = gr.Chatbot(height=500)
+    with gr.Row():
+        with gr.Column():
+            msg = gr.Textbox(
+                label="Chat Message Box",
+                placeholder="Chat Message Box",
+                show_label=False,
+                interactive=enabled,
+                container=False,
+            )
+        with gr.Column():
+            with gr.Row():
+                submit = gr.Button("Submit", interactive=enabled)
+                stop = gr.Button("Stop", interactive=enabled)
+                clear = gr.Button("Clear", interactive=enabled)
+
+    with gr.Row(visible=False):
+        with gr.Group():
+            config_file = gr.File(label="Upload sharding configuration", visible=False)
+            json_view_button = gr.Button("View as JSON", visible=False)
+        json_view = gr.JSON(visible=False)
+        json_view_button.click(
+            fn=view_json_file, inputs=[config_file], outputs=[json_view]
+        )
+    submit_event = msg.submit(
+        fn=user,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        show_progress=False,
+        queue=False,
+    ).then(
+        fn=chat_fn,
+        inputs=[
+            prompt_prefix,
+            chatbot,
+            model,
+            device,
+            precision,
+            download_vmfb,
+            config_file,
+            streaming_llm,
+        ],
+        outputs=[chatbot, tokens_time],
+        show_progress=False,
+        queue=True,
+    )
+    submit_click_event = submit.click(
+        fn=user,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        show_progress=False,
+        queue=False,
+    ).then(
+        fn=chat_fn,
+        inputs=[
+            prompt_prefix,
+            chatbot,
+            model,
+            device,
+            precision,
+            download_vmfb,
+            config_file,
+            streaming_llm,
+        ],
+        outputs=[chatbot, tokens_time],
+        show_progress=False,
+        queue=True,
+    )
+    stop.click(
+        fn=None,
+        inputs=None,
+        outputs=None,
+        cancels=[submit_event, submit_click_event],
+        queue=False,
+    )
+    clear.click(
+        fn=chat_fn,
+        inputs=[
+            clear,
+            chatbot,
+            model,
+            device,
+            precision,
+            download_vmfb,
+            config_file,
+            streaming_llm,
+        ],
+        outputs=[chatbot, tokens_time],
+        show_progress=False,
+        queue=True,
+    ).then(lambda: None, None, [chatbot], queue=False)

apps/shark_studio/web/ui/common_events.py

@@ -0,0 +1,67 @@
+from apps.shark_studio.web.ui.utils import (
+    HSLHue,
+    hsl_color,
+)
+from apps.shark_studio.modules.embeddings import get_lora_metadata
+
+
+# Answers HTML to show the most frequent tags used when a LoRA was trained,
+# taken from the metadata of its .safetensors file.
+def lora_changed(lora_files):
+    # tag frequency percentage, that gets maximum amount of the staring hue
+    TAG_COLOR_THRESHOLD = 0.55
+    # tag frequency percentage, above which a tag is displayed
+    TAG_DISPLAY_THRESHOLD = 0.65
+    # template for the html used to display a tag
+    TAG_HTML_TEMPLATE = (
+        '<span class="lora-tag" style="border: 1px solid {color};">{tag}</span>'
+    )
+    output = []
+    for lora_file in lora_files:
+        if lora_file == "":
+            output.extend(["<div><i>No LoRA selected</i></div>"])
+        elif not lora_file.lower().endswith(".safetensors"):
+            output.extend(
+                [
+                    "<div><i>Only metadata queries for .safetensors files are currently supported</i></div>"
+                ]
+            )
+        else:
+            metadata = get_lora_metadata(lora_file)
+            if metadata:
+                frequencies = metadata["frequencies"]
+                output.extend(
+                    [
+                        "".join(
+                            [
+                                f'<div class="lora-model">Trained against weights in: {metadata["model"]}</div>'
+                            ]
+                            + [
+                                TAG_HTML_TEMPLATE.format(
+                                    color=hsl_color(
+                                        (tag[1] - TAG_COLOR_THRESHOLD)
+                                        / (1 - TAG_COLOR_THRESHOLD),
+                                        start=HSLHue.RED,
+                                        end=HSLHue.GREEN,
+                                    ),
+                                    tag=tag[0],
+                                )
+                                for tag in frequencies
+                                if tag[1] > TAG_DISPLAY_THRESHOLD
+                            ],
+                        )
+                    ]
+                )
+            elif metadata is None:
+                output.extend(
+                    [
+                        "<div><i>This LoRA does not publish tag frequency metadata</i></div>"
+                    ]
+                )
+            else:
+                output.extend(
+                    [
+                        "<div><i>This LoRA has empty tag frequency metadata, or we could not parse it</i></div>"
+                    ]
+                )
+    return output

apps/shark_studio/web/ui/css/sd_dark_theme.css

@@ -0,0 +1,373 @@
+/*
+Apply Gradio dark theme to the default Gradio theme.
+Procedure to upgrade the dark theme:
+- Using your browser, visit http://localhost:8080/?__theme=dark
+- Open your browser inspector, search for the .dark css class
+- Copy .dark class declarations, apply them here into :root
+*/
+
+:root {
+    --body-background-fill: var(--background-fill-primary);
+    --body-text-color: var(--neutral-100);
+    --color-accent-soft: var(--neutral-700);
+    --background-fill-primary: var(--neutral-950);
+    --background-fill-secondary: var(--neutral-900);
+    --border-color-accent: var(--neutral-600);
+    --border-color-primary: var(--neutral-700);
+    --link-text-color-active: var(--secondary-500);
+    --link-text-color: var(--secondary-500);
+    --link-text-color-hover: var(--secondary-400);
+    --link-text-color-visited: var(--secondary-600);
+    --body-text-color-subdued: var(--neutral-400);
+    --shadow-spread: 1px;
+    --block-background-fill: var(--neutral-800);
+    --block-border-color: var(--border-color-primary);
+    --block_border_width: None;
+    --block-info-text-color: var(--body-text-color-subdued);
+    --block-label-background-fill: var(--background-fill-secondary);
+    --block-label-border-color: var(--border-color-primary);
+    --block_label_border_width: None;
+    --block-label-text-color: var(--neutral-200);
+    --block_shadow: None;
+    --block_title_background_fill: None;
+    --block_title_border_color: None;
+    --block_title_border_width: None;
+    --block-title-text-color: var(--neutral-200);
+    --panel-background-fill: var(--background-fill-secondary);
+    --panel-border-color: var(--border-color-primary);
+    --panel_border_width: None;
+    --checkbox-background-color: var(--neutral-800);
+    --checkbox-background-color-focus: var(--checkbox-background-color);
+    --checkbox-background-color-hover: var(--checkbox-background-color);
+    --checkbox-background-color-selected: var(--secondary-600);
+    --checkbox-border-color: var(--neutral-700);
+    --checkbox-border-color-focus: var(--secondary-500);
+    --checkbox-border-color-hover: var(--neutral-600);
+    --checkbox-border-color-selected: var(--secondary-600);
+    --checkbox-border-width: var(--input-border-width);
+    --checkbox-label-background-fill: linear-gradient(to top, var(--neutral-900), var(--neutral-800));
+    --checkbox-label-background-fill-hover: linear-gradient(to top, var(--neutral-900), var(--neutral-800));
+    --checkbox-label-background-fill-selected: var(--checkbox-label-background-fill);
+    --checkbox-label-border-color: var(--border-color-primary);
+    --checkbox-label-border-color-hover: var(--checkbox-label-border-color);
+    --checkbox-label-border-width: var(--input-border-width);
+    --checkbox-label-text-color: var(--body-text-color);
+    --checkbox-label-text-color-selected: var(--checkbox-label-text-color);
+    --error-background-fill: var(--background-fill-primary);
+    --error-border-color: var(--border-color-primary);
+    --error_border_width: None;
+    --error-text-color: #ef4444;
+    --input-background-fill: var(--neutral-800);
+    --input-background-fill-focus: var(--secondary-600);
+    --input-background-fill-hover: var(--input-background-fill);
+    --input-border-color: var(--border-color-primary);
+    --input-border-color-focus: var(--neutral-700);
+    --input-border-color-hover: var(--input-border-color);
+    --input_border_width: None;
+    --input-placeholder-color: var(--neutral-500);
+    --input_shadow: None;
+    --input-shadow-focus: 0 0 0 var(--shadow-spread) var(--neutral-700), var(--shadow-inset);
+    --loader_color: None;
+    --slider_color: None;
+    --stat-background-fill: linear-gradient(to right, var(--primary-400), var(--primary-600));
+    --table-border-color: var(--neutral-700);
+    --table-even-background-fill: var(--neutral-950);
+    --table-odd-background-fill: var(--neutral-900);
+    --table-row-focus: var(--color-accent-soft);
+    --button-border-width: var(--input-border-width);
+    --button-cancel-background-fill: linear-gradient(to bottom right, #dc2626, #b91c1c);
+    --button-cancel-background-fill-hover: linear-gradient(to bottom right, #dc2626, #dc2626);
+    --button-cancel-border-color: #dc2626;
+    --button-cancel-border-color-hover: var(--button-cancel-border-color);
+    --button-cancel-text-color: white;
+    --button-cancel-text-color-hover: var(--button-cancel-text-color);
+    --button-primary-background-fill: linear-gradient(to bottom right, var(--primary-500), var(--primary-600));
+    --button-primary-background-fill-hover: linear-gradient(to bottom right, var(--primary-500), var(--primary-500));
+    --button-primary-border-color: var(--primary-500);
+    --button-primary-border-color-hover: var(--button-primary-border-color);
+    --button-primary-text-color: white;
+    --button-primary-text-color-hover: var(--button-primary-text-color);
+    --button-secondary-background-fill: linear-gradient(to bottom right, var(--neutral-600), var(--neutral-700));
+    --button-secondary-background-fill-hover: linear-gradient(to bottom right, var(--neutral-600), var(--neutral-600));
+    --button-secondary-border-color: var(--neutral-600);
+    --button-secondary-border-color-hover: var(--button-secondary-border-color);
+    --button-secondary-text-color: white;
+    --button-secondary-text-color-hover: var(--button-secondary-text-color);
+    --block-border-width: 1px;
+    --block-label-border-width: 1px;
+    --form-gap-width: 1px;
+    --error-border-width: 1px;
+    --input-border-width: 1px;
+}
+
+/* SHARK theme */
+body {
+    background-color: var(--background-fill-primary);
+}
+
+.generating.svelte-zlszon.svelte-zlszon {
+    border: none;
+}
+
+.generating {
+    border: none !important;
+}
+
+#chatbot {
+    height: 100% !important;
+}
+
+/* display in full width for desktop devices, but see below */
+@media (min-width: 1536px)
+{
+    .gradio-container {
+        max-width: var(--size-full) !important;
+    }
+}
+
+/* media rules in custom css are don't appear to be applied in
+   gradio versions > 4.7, so we have to define a class which
+   we will manually need add and remove using javascript.
+   Remove this once this fixed in gradio.
+*/
+.gradio-container-size-full {
+    max-width: var(--size-full) !important;
+}
+
+.gradio-container .contain {
+    padding: 0 var(--size-4) !important;
+}
+
+#top_logo {
+    color: transparent;
+    background-color: transparent;
+    border-radius: 0 !important;
+    border: 0;
+}
+
+#ui_title {
+    padding: var(--size-2) 0 0 var(--size-1);
+}
+
+#demo_title_outer {
+    border-radius: 0;
+}
+
+#prompt_box_outer div:first-child {
+    border-radius: 0 !important
+}
+
+#prompt_box textarea, #negative_prompt_box textarea {
+    background-color: var(--background-fill-primary) !important;
+}
+
+#prompt_examples {
+    margin: 0 !important;
+}
+
+#prompt_examples svg {
+    display: none !important;
+}
+
+#ui_body {
+    padding: var(--size-2) !important;
+    border-radius: 0.5em !important;
+}
+
+#img_result+div {
+    display: none !important;
+}
+
+footer {
+    display: none !important;
+}
+
+#gallery + div {
+    border-radius: 0 !important;
+}
+
+/* Gallery: Remove the default square ratio thumbnail and limit images height to the container */
+#gallery .thumbnail-item.thumbnail-lg {
+    aspect-ratio: unset;
+    max-height: calc(55vh - (2 * var(--spacing-lg)));
+}
+/* fix width and height of gallery items when on very large desktop screens, but see below */
+@media (min-width: 1921px) {
+    /* Force a 768px_height + 4px_margin_height + navbar_height for the gallery */
+    #gallery .grid-wrap, #gallery .preview{
+        min-height: calc(768px + 4px + var(--size-14));
+        max-height: calc(768px + 4px + var(--size-14));
+    }
+    /* Limit height to 768px_height + 2px_margin_height for the thumbnails */
+    #gallery .thumbnail-item.thumbnail-lg {
+        max-height: 770px !important;
+    }
+}
+
+/* media rules in custom css are don't appear to be applied in
+   gradio versions > 4.7, so we have to define classes which
+   we will manually need add and remove using javascript.
+   Remove this once this fixed in gradio.
+*/
+.gallery-force-height768 .grid-wrap, .gallery-force-height768 .preview {
+    min-height: calc(768px + 4px + var(--size-14)) !important;
+    max-height: calc(768px + 4px + var(--size-14)) !important;
+}
+.gallery-limit-height768 .thumbnail-item.thumbnail-lg {
+    max-height: 770px !important;
+}
+
+/* Don't upscale when viewing in solo image mode */
+#gallery .preview img {
+    object-fit: scale-down;
+}
+/* Navbar images in cover mode*/
+#gallery .preview .thumbnail-item img {
+    object-fit: cover;
+}
+
+/* Limit the stable diffusion text output height */
+#std_output textarea {
+    max-height: 215px;
+}
+
+/* Prevent progress bar to block gallery navigation while building images (Gradio V3.19.0) */
+#gallery .wrap.default {
+    pointer-events: none;
+}
+
+/* Import Png info box */
+#txt2img_prompt_image {
+    height: var(--size-32) !important;
+}
+
+/* Hide "remove buttons" from ui dropdowns */
+#custom_model .token-remove.remove-all,
+#lora_weights .token-remove.remove-all,
+#scheduler .token-remove.remove-all,
+#device .token-remove.remove-all,
+#stencil_model .token-remove.remove-all {
+    display: none;
+}
+
+/* Hide selected items from ui dropdowns */
+#custom_model .options .item .inner-item,
+#scheduler .options .item .inner-item,
+#device .options .item .inner-item,
+#stencil_model .options .item .inner-item {
+    display:none;
+}
+
+/* workarounds for container=false not currently working for dropdowns */
+.dropdown_no_container {
+    padding: 0 !important;
+}
+
+#output_subdir_container :first-child {
+    border: none;
+}
+
+/* reduced animation load when generating */
+.generating {
+    animation-play-state: paused !important;
+}
+
+/* better clarity when progress bars are minimal */
+.meta-text {
+    background-color: var(--block-label-background-fill);
+}
+
+/* lora tag pills */
+.lora-tags {
+    border: 1px solid var(--border-color-primary);
+    color: var(--block-info-text-color) !important;
+    padding: var(--block-padding);
+}
+
+.lora-tag {
+    display: inline-block;
+    height: 2em;
+    color: rgb(212 212 212) !important;
+    margin-right: 5pt;
+    margin-bottom: 5pt;
+    padding: 2pt 5pt;
+    border-radius: 5pt;
+    white-space: nowrap;
+}
+
+.lora-model {
+    margin-bottom: var(--spacing-lg);
+    color: var(--block-info-text-color) !important;
+    line-height: var(--line-sm);
+}
+
+/* output gallery tab */
+.output_parameters_dataframe table.table {
+    /* works around a gradio bug that always shows scrollbars */
+    overflow: clip auto;
+}
+
+.output_parameters_dataframe tbody td {
+    font-size: small;
+    line-height: var(--line-xs);
+}
+
+.output_icon_button {
+    max-width: 30px;
+    align-self: end;
+    padding-bottom: 8px;
+}
+
+.outputgallery_sendto {
+    min-width: 7em !important;
+}
+
+/* output gallery should take up most of the viewport height regardless of image size/number */
+#outputgallery_gallery .fixed-height {
+    min-height: 89vh !important;
+}
+
+.sd-right-panel {
+    height: calc(100vmin - var(--size-32) - var(--size-10)) !important;
+    overflow-y: scroll;
+}
+
+.sd-right-panel .fill {
+    flex: 1;
+}
+
+/* don't stretch non-square images to be square, breaking their aspect ratio */
+#outputgallery_gallery .thumbnail-item.thumbnail-lg > img {
+    object-fit: contain !important;
+}
+
+/* centered logo for when there are no images */
+#top_logo.logo_centered {
+    height: 100%;
+    width: 100%;
+}
+
+#top_logo.logo_centered img {
+    object-fit: scale-down;
+    position: absolute;
+    width: 80%;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+}
+
+#tab_bar_logo {
+    overflow: visible !important;
+    border-width: 0 !important;
+    height: 0px !important;
+    padding: 0;
+    margin: 0;
+}
+
+#tab_bar_logo .image-container {
+    object-fit: scale-down;
+    position: absolute !important;
+    top: 10px;
+    right: 0px;
+    height: 36px;
+}

apps/shark_studio/web/ui/js/sd_gradio_workarounds.js

@@ -0,0 +1,49 @@
+// workaround gradio after 4.7, not applying any @media rules form the custom .css file
+
+() => {
+    console.log(`innerWidth: ${window.innerWidth}` )
+
+    // 1536px rules
+
+    const mediaQuery1536 = window.matchMedia('(min-width: 1536px)')
+
+    function handleWidth1536(event) {
+
+        // display in full width for desktop devices
+        document.querySelectorAll(".gradio-container")
+            .forEach( (node) => {
+                if (event.matches) {
+                    node.classList.add("gradio-container-size-full");
+                } else {
+                    node.classList.remove("gradio-container-size-full")
+                }
+            });
+    }
+
+    mediaQuery1536.addEventListener("change", handleWidth1536);
+    mediaQuery1536.dispatchEvent(new MediaQueryListEvent("change", {matches: window.innerWidth >= 1536}));
+
+    // 1921px rules
+
+    const mediaQuery1921 = window.matchMedia('(min-width: 1921px)')
+
+    function handleWidth1921(event) {
+
+        /* Force a 768px_height + 4px_margin_height + navbar_height for the gallery */
+        /* Limit height to 768px_height + 2px_margin_height for the thumbnails */
+        document.querySelectorAll("#gallery")
+            .forEach( (node) => {
+                if (event.matches) {
+                    node.classList.add("gallery-force-height768");
+                    node.classList.add("gallery-limit-height768");
+                } else {
+                    node.classList.remove("gallery-force-height768");
+                    node.classList.remove("gallery-limit-height768");
+                }
+            });
+    }
+
+    mediaQuery1921.addEventListener("change", handleWidth1921);
+    mediaQuery1921.dispatchEvent(new MediaQueryListEvent("change", {matches: window.innerWidth >= 1921}));
+
+}

apps/shark_studio/web/ui/outputgallery.py

@@ -0,0 +1,406 @@
+import glob
+import gradio as gr
+import os
+import subprocess
+import sys
+from PIL import Image
+
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+from apps.shark_studio.web.utils.file_utils import (
+    get_generated_imgs_path,
+    get_generated_imgs_todays_subdir,
+)
+from apps.shark_studio.web.ui.utils import amdlogo_loc
+from apps.shark_studio.web.utils.metadata import displayable_metadata
+
+# -- Functions for file, directory and image info querying
+
+output_dir = get_generated_imgs_path()
+
+
+def outputgallery_filenames(subdir) -> list[str]:
+    new_dir_path = os.path.join(output_dir, subdir)
+    if os.path.exists(new_dir_path):
+        filenames = [
+            glob.glob(new_dir_path + "/" + ext) for ext in ("*.png", "*.jpg", "*.jpeg")
+        ]
+
+        return sorted(sum(filenames, []), key=os.path.getmtime, reverse=True)
+    else:
+        return []
+
+
+def output_subdirs() -> list[str]:
+    # Gets a list of subdirectories of output_dir and below, as relative paths.
+    relative_paths = [
+        os.path.relpath(entry[0], output_dir)
+        for entry in os.walk(
+            output_dir, followlinks=cmd_opts.output_gallery_followlinks
+        )
+    ]
+
+    # It is less confusing to always including the subdir that will take any
+    # images generated today even if it doesn't exist yet
+    if get_generated_imgs_todays_subdir() not in relative_paths:
+        relative_paths.append(get_generated_imgs_todays_subdir())
+
+    # sort subdirectories so that the date named ones we probably
+    # created in this or previous sessions come first, sorted with the most
+    # recent first. Other subdirs are listed after.
+    generated_paths = sorted(
+        [path for path in relative_paths if path.isnumeric()], reverse=True
+    )
+    result_paths = generated_paths + sorted(
+        [path for path in relative_paths if (not path.isnumeric()) and path != "."]
+    )
+
+    return result_paths
+
+
+# --- Define UI layout for Gradio
+
+with gr.Blocks() as outputgallery_element:
+    amd_logo = Image.open(amdlogo_loc)
+
+    with gr.Row(elem_id="outputgallery_gallery"):
+        # needed to workaround gradio issue:
+        # https://github.com/gradio-app/gradio/issues/2907
+        dev_null = gr.Textbox("", visible=False)
+
+        gallery_files = gr.State(value=[])
+        subdirectory_paths = gr.State(value=[])
+
+        with gr.Column(scale=6):
+            logo = gr.Image(
+                label="Getting subdirectories...",
+                value=amd_logo,
+                interactive=False,
+                visible=True,
+                show_label=True,
+                elem_id="top_logo",
+                elem_classes="logo_centered",
+                show_download_button=False,
+            )
+
+            gallery = gr.Gallery(
+                label="",
+                value=gallery_files.value,
+                visible=False,
+                show_label=True,
+                columns=4,
+            )
+
+        with gr.Column(scale=4):
+            with gr.Group():
+                with gr.Row():
+                    with gr.Column(
+                        scale=15,
+                        min_width=160,
+                        elem_id="output_subdir_container",
+                    ):
+                        subdirectories = gr.Dropdown(
+                            label=f"Subdirectories of {output_dir}",
+                            type="value",
+                            choices=subdirectory_paths.value,
+                            value="",
+                            interactive=True,
+                            elem_classes="dropdown_no_container",
+                            allow_custom_value=True,
+                        )
+                    with gr.Column(
+                        scale=1,
+                        min_width=32,
+                        elem_classes="output_icon_button",
+                    ):
+                        open_subdir = gr.Button(
+                            variant="secondary",
+                            value="\U0001F5C1",  # unicode open folder
+                            interactive=False,
+                            size="sm",
+                        )
+                    with gr.Column(
+                        scale=1,
+                        min_width=32,
+                        elem_classes="output_icon_button",
+                    ):
+                        refresh = gr.Button(
+                            variant="secondary",
+                            value="\u21BB",  # unicode clockwise arrow circle
+                            size="sm",
+                        )
+
+            image_columns = gr.Slider(
+                label="Columns shown", value=4, minimum=1, maximum=16, step=1
+            )
+            outputgallery_filename = gr.Textbox(
+                label="Filename",
+                value="None",
+                interactive=False,
+                show_copy_button=True,
+            )
+
+            with gr.Accordion(
+                label="Parameter Information", open=False
+            ) as parameters_accordian:
+                image_parameters = gr.DataFrame(
+                    headers=["Parameter", "Value"],
+                    col_count=2,
+                    wrap=True,
+                    elem_classes="output_parameters_dataframe",
+                    value=[["Status", "No image selected"]],
+                    interactive=True,
+                )
+
+            with gr.Accordion(label="Send To", open=True):
+                with gr.Row():
+                    outputgallery_sendto_sd = gr.Button(
+                        value="Stable Diffusion",
+                        interactive=False,
+                        elem_classes="outputgallery_sendto",
+                        size="sm",
+                    )
+
+    # --- Event handlers
+
+    def on_clear_gallery():
+        return [
+            gr.Gallery(
+                value=[],
+                visible=False,
+            ),
+            gr.Image(
+                visible=True,
+            ),
+        ]
+
+    def on_image_columns_change(columns):
+        return gr.Gallery(columns=columns)
+
+    def on_select_subdir(subdir) -> list:
+        # evt.value is the subdirectory name
+        new_images = outputgallery_filenames(subdir)
+        new_label = f"{len(new_images)} images in {os.path.join(output_dir, subdir)}"
+        return [
+            new_images,
+            gr.Gallery(
+                value=new_images,
+                label=new_label,
+                visible=len(new_images) > 0,
+            ),
+            gr.Image(
+                label=new_label,
+                visible=len(new_images) == 0,
+            ),
+        ]
+
+    def on_open_subdir(subdir):
+        subdir_path = os.path.normpath(os.path.join(output_dir, subdir))
+
+        if os.path.isdir(subdir_path):
+            if sys.platform == "linux":
+                subprocess.run(["xdg-open", subdir_path])
+            elif sys.platform == "darwin":
+                subprocess.run(["open", subdir_path])
+            elif sys.platform == "win32":
+                os.startfile(subdir_path)
+
+    def on_refresh(current_subdir: str) -> list:
+        # get an up-to-date subdirectory list
+        refreshed_subdirs = output_subdirs()
+        # get the images using either the current subdirectory or the most
+        # recent valid one
+        new_subdir = (
+            current_subdir
+            if current_subdir in refreshed_subdirs
+            else refreshed_subdirs[0]
+        )
+        new_images = outputgallery_filenames(new_subdir)
+        new_label = (
+            f"{len(new_images)} images in " f"{os.path.join(output_dir, new_subdir)}"
+        )
+
+        return [
+            gr.Dropdown(
+                choices=refreshed_subdirs,
+                value=new_subdir,
+            ),
+            refreshed_subdirs,
+            new_images,
+            gr.Gallery(value=new_images, label=new_label, visible=len(new_images) > 0),
+            gr.Image(
+                label=new_label,
+                visible=len(new_images) == 0,
+            ),
+        ]
+
+    def on_new_image(subdir, subdir_paths, status) -> list:
+        # prevent error triggered when an image generates before the tab
+        # has even been selected
+        subdir_paths = (
+            subdir_paths
+            if len(subdir_paths) > 0
+            else [get_generated_imgs_todays_subdir()]
+        )
+
+        # only update if the current subdir is the most recent one as
+        # new images only go there
+        if subdir_paths[0] == subdir:
+            new_images = outputgallery_filenames(subdir)
+            new_label = (
+                f"{len(new_images)} images in "
+                f"{os.path.join(output_dir, subdir)} - {status}"
+            )
+
+            return [
+                new_images,
+                gr.Gallery(
+                    value=new_images,
+                    label=new_label,
+                    visible=len(new_images) > 0,
+                ),
+                gr.Image(
+                    label=new_label,
+                    visible=len(new_images) == 0,
+                ),
+            ]
+        else:
+            # otherwise change nothing,
+            # (only untyped gradio gr.update() does this)
+            return [gr.update(), gr.update(), gr.update()]
+
+    def on_select_image(images: list[str], evt: gr.SelectData) -> list:
+        # evt.index is an index into the full list of filenames for
+        # the current subdirectory
+        filename = images[evt.index]
+        params = displayable_metadata(filename)
+
+        if params:
+            if params["source"] == "missing":
+                return [
+                    "Could not find this image file, refresh the gallery and update the images",
+                    [["Status", "File missing"]],
+                ]
+            else:
+                return [
+                    filename,
+                    list(map(list, params["parameters"].items())),
+                ]
+
+        return [
+            filename,
+            [["Status", "No parameters found"]],
+        ]
+
+    def on_outputgallery_filename_change(filename: str) -> list:
+        exists = filename != "None" and os.path.exists(filename)
+        return [
+            # disable or enable each of the sendto button based on whether
+            # an image is selected
+            gr.Button(interactive=exists),
+        ]
+
+    # The time first our tab is selected we need to do an initial refresh
+    # to populate the subdirectory select box and the images from the most
+    # recent subdirectory.
+    #
+    # We do it at this point rather than setting this up in the controls'
+    # definitions as when you refresh the browser you always get what was
+    # *initially* set, which won't include any new subdirectories or images
+    # that might have created since the application was started. Doing it
+    # this way means a browser refresh/reload always gets the most
+    # up-to-date data.
+    def on_select_tab(subdir_paths, request: gr.Request):
+        local_client = request.headers["host"].startswith(
+            "127.0.0.1:"
+        ) or request.headers["host"].startswith("localhost:")
+
+        if len(subdir_paths) == 0:
+            return on_refresh("") + [gr.update(interactive=local_client)]
+        else:
+            return (
+                # Change nothing, (only untyped gr.update() does this)
+                gr.update(),
+                gr.update(),
+                gr.update(),
+                gr.update(),
+                gr.update(),
+                gr.update(),
+            )
+
+    # clearing images when we need to completely change what's in the
+    # gallery avoids current images being shown replacing piecemeal and
+    # prevents weirdness and errors if the user selects an image during the
+    # replacement phase.
+    clear_gallery = dict(
+        fn=on_clear_gallery,
+        inputs=None,
+        outputs=[gallery, logo],
+        queue=False,
+    )
+
+    subdirectories.select(**clear_gallery).then(
+        on_select_subdir,
+        [subdirectories],
+        [gallery_files, gallery, logo],
+        queue=False,
+    )
+
+    open_subdir.click(on_open_subdir, inputs=[subdirectories], queue=False)
+
+    refresh.click(**clear_gallery).then(
+        on_refresh,
+        [subdirectories],
+        [subdirectories, subdirectory_paths, gallery_files, gallery, logo],
+        queue=False,
+    )
+
+    image_columns.change(
+        fn=on_image_columns_change,
+        inputs=[image_columns],
+        outputs=[gallery],
+        queue=False,
+    )
+
+    gallery.select(
+        on_select_image,
+        [gallery_files],
+        [outputgallery_filename, image_parameters],
+        queue=False,
+    )
+
+    outputgallery_filename.change(
+        on_outputgallery_filename_change,
+        [outputgallery_filename],
+        [
+            outputgallery_sendto_sd,
+        ],
+        queue=False,
+    )
+
+    # We should have been given the .select function for our tab, so set it up
+    def outputgallery_tab_select(select):
+        select(
+            fn=on_select_tab,
+            inputs=[subdirectory_paths],
+            outputs=[
+                subdirectories,
+                subdirectory_paths,
+                gallery_files,
+                gallery,
+                logo,
+                open_subdir,
+            ],
+            queue=False,
+        )
+
+    # We should have been passed a list of components on other tabs that update
+    # when a new image has generated on that tab, so set things up so the user
+    # will see that new image if they are looking at today's subdirectory
+    def outputgallery_watch(components: gr.Textbox):
+        for component in components:
+            component.change(
+                on_new_image,
+                inputs=[subdirectories, subdirectory_paths, component],
+                outputs=[gallery_files, gallery, logo],
+                queue=False,
+            )

apps/shark_studio/web/ui/sd.py

@@ -0,0 +1,866 @@
+import os
+import json
+import gradio as gr
+import numpy as np
+from inspect import signature
+from PIL import Image
+from pathlib import Path
+from datetime import datetime as dt
+from gradio.components.image_editor import (
+    EditorValue,
+)
+from apps.shark_studio.web.utils.file_utils import (
+    get_generated_imgs_path,
+    get_checkpoints_path,
+    get_checkpoints,
+    get_configs_path,
+    get_configs,
+    write_default_sd_configs,
+)
+from apps.shark_studio.api.sd import (
+    shark_sd_fn_dict_input,
+    cancel_sd,
+    unload_sd,
+)
+from apps.shark_studio.api.controlnet import (
+    cnet_preview,
+)
+from apps.shark_studio.modules.schedulers import (
+    scheduler_model_map,
+)
+from apps.shark_studio.modules.img_processing import (
+    resampler_list,
+    resize_stencil,
+)
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+from apps.shark_studio.web.ui.utils import (
+    amdlogo_loc,
+    none_to_str_none,
+    str_none_to_none,
+)
+from apps.shark_studio.web.utils.state import (
+    status_label,
+)
+from apps.shark_studio.web.ui.common_events import lora_changed
+from apps.shark_studio.modules import logger
+import apps.shark_studio.web.utils.globals as global_obj
+
+# Disabled some models for demo purposes
+sd_default_models = [
+    # "runwayml/stable-diffusion-v1-5",
+    # "stabilityai/stable-diffusion-2-1-base",
+    # "stabilityai/stable-diffusion-2-1",
+    # "stabilityai/stable-diffusion-xl-base-1.0",
+    # "stabilityai/sdxl-turbo",
+]
+sd_default_models.extend(get_checkpoints(model_type="scripts"))
+
+
+def view_json_file(file_path):
+    content = ""
+    with open(file_path, "r") as fopen:
+        content = fopen.read()
+    return content
+
+
+def submit_to_cnet_config(
+    stencil: str,
+    preprocessed_hint: str,
+    cnet_strength: int,
+    control_mode: str,
+    curr_config: dict,
+):
+    if any(i in [None, ""] for i in [stencil, preprocessed_hint]):
+        return gr.update()
+    if curr_config is not None:
+        if "controlnets" in curr_config:
+            curr_config["controlnets"]["control_mode"] = control_mode
+            curr_config["controlnets"]["model"].append(stencil)
+            curr_config["controlnets"]["hint"].append(preprocessed_hint)
+            curr_config["controlnets"]["strength"].append(cnet_strength)
+            return curr_config
+
+    cnet_map = {}
+    cnet_map["controlnets"] = {
+        "control_mode": control_mode,
+        "model": [stencil],
+        "hint": [preprocessed_hint],
+        "strength": [cnet_strength],
+    }
+    return cnet_map
+
+
+def update_embeddings_json(embedding):
+    return {"embeddings": [embedding]}
+
+
+def submit_to_main_config(input_cfg: dict, main_cfg: dict):
+    if main_cfg in [None, "", {}]:
+        return input_cfg
+
+    for base_key in input_cfg:
+        main_cfg[base_key] = input_cfg[base_key]
+    return main_cfg
+
+
+def pull_sd_configs(
+    prompt,
+    negative_prompt,
+    sd_init_image,
+    height,
+    width,
+    steps,
+    strength,
+    guidance_scale,
+    seed,
+    batch_count,
+    batch_size,
+    scheduler,
+    base_model_id,
+    custom_weights,
+    custom_vae,
+    precision,
+    device,
+    target_triple,
+    ondemand,
+    compiled_pipeline,
+    resample_type,
+    controlnets,
+    embeddings,
+):
+    sd_args = str_none_to_none(locals())
+    sd_cfg = {}
+    for arg in sd_args:
+        if arg in [
+            "prompt",
+            "negative_prompt",
+            "sd_init_image",
+        ]:
+            sd_cfg[arg] = [sd_args[arg]]
+        elif arg in ["controlnets", "embeddings"]:
+            if isinstance(arg, dict):
+                sd_cfg[arg] = json.loads(sd_args[arg])
+            else:
+                sd_cfg[arg] = {}
+        else:
+            sd_cfg[arg] = sd_args[arg]
+
+    return json.dumps(sd_cfg)
+
+
+def load_sd_cfg(sd_json: dict, load_sd_config: str):
+    if os.path.exists(load_sd_config):
+        config = load_sd_config
+    elif os.path.exists(os.path.join(get_configs_path(), load_sd_config)):
+        config = os.path.join(get_configs_path(), load_sd_config)
+    else:
+        print(
+            "Default config not found as absolute path or in configs folder. Using sdxl-turbo as default config."
+        )
+        config = sd_json
+    new_sd_config = none_to_str_none(json.loads(view_json_file(config)))
+    if sd_json:
+        for key in new_sd_config:
+            sd_json[key] = new_sd_config[key]
+    else:
+        sd_json = new_sd_config
+    for i in sd_json["sd_init_image"]:
+        if i is not None:
+            if os.path.isfile(i):
+                sd_image = [Image.open(i, mode="r")]
+    else:
+        sd_image = None
+    if not sd_json["device"]:
+        sd_json["device"] = gr.update()
+
+    return [
+        sd_json["prompt"][0],
+        sd_json["negative_prompt"][0],
+        sd_image,
+        sd_json["height"],
+        sd_json["width"],
+        gr.update(),
+        sd_json["strength"],
+        sd_json["guidance_scale"],
+        sd_json["seed"],
+        sd_json["batch_count"],
+        sd_json["batch_size"],
+        sd_json["scheduler"],
+        sd_json["base_model_id"],
+        sd_json["custom_weights"],
+        sd_json["custom_vae"],
+        sd_json["precision"],
+        sd_json["device"],
+        sd_json["target_triple"],
+        sd_json["ondemand"],
+        sd_json["compiled_pipeline"],
+        sd_json["resample_type"],
+        sd_json["controlnets"],
+        sd_json["embeddings"],
+        sd_json,
+    ]
+
+
+def save_sd_cfg(config: dict, save_name: str):
+    if os.path.exists(save_name):
+        filepath = save_name
+    elif cmd_opts.configs_path:
+        filepath = os.path.join(cmd_opts.configs_path, save_name)
+    else:
+        filepath = os.path.join(get_configs_path(), save_name)
+    if ".json" not in filepath:
+        filepath += ".json"
+    with open(filepath, mode="w") as f:
+        f.write(json.dumps(config))
+    return save_name
+
+
+def create_canvas(width, height):
+    data = Image.fromarray(
+        np.zeros(
+            shape=(height, width, 3),
+            dtype=np.uint8,
+        )
+        + 255
+    )
+    img_dict = {
+        "background": data,
+        "layers": [],
+        "composite": None,
+    }
+    return EditorValue(img_dict)
+
+
+def import_original(original_img, width, height):
+    if original_img is None:
+        resized_img = create_canvas(width, height)
+        return resized_img
+    else:
+        resized_img, _, _ = resize_stencil(original_img, width, height)
+        img_dict = {
+            "background": resized_img,
+            "layers": [],
+            "composite": None,
+        }
+        return EditorValue(img_dict)
+
+
+def base_model_changed(base_model_id):
+    new_choices = get_checkpoints(
+        os.path.join("checkpoints", os.path.basename(str(base_model_id)))
+    ) + get_checkpoints(model_type="checkpoints")
+    if "turbo" in base_model_id:
+        new_steps = gr.Dropdown(
+            value=2,
+            choices=[1, 2],
+            label="\U0001F3C3\U0000FE0F Steps",
+            allow_custom_value=True,
+        )
+    if "stable-diffusion-xl-base-1.0" in base_model_id:
+        new_steps = gr.Dropdown(
+            value=40,
+            choices=[20, 25, 30, 35, 40, 45, 50],
+            label="\U0001F3C3\U0000FE0F Steps",
+            allow_custom_value=True,
+        )
+    elif ".py" in base_model_id:
+        new_steps = gr.Dropdown(
+            value=20,
+            choices=[10, 15, 20],
+            label="\U0001F3C3\U0000FE0F Steps",
+            allow_custom_value=True,
+        )
+    else:
+        new_steps = gr.Dropdown(
+            value=20,
+            choices=[10, 20, 30, 40, 50],
+            label="\U0001F3C3\U0000FE0F Steps",
+            allow_custom_value=True,
+        )
+
+    return [
+        gr.Dropdown(
+            value=new_choices[0] if len(new_choices) > 0 else "None",
+            choices=["None"] + new_choices,
+        ),
+        new_steps,
+    ]
+
+
+init_config = global_obj.get_init_config()
+init_config = none_to_str_none(json.loads(view_json_file(init_config)))
+
+with gr.Blocks(title="Stable Diffusion") as sd_element:
+    with gr.Column(elem_id="ui_body"):
+        with gr.Row():
+            with gr.Column(scale=2, min_width=600):
+                with gr.Group(elem_id="prompt_box_outer"):
+                    prompt = gr.Textbox(
+                        label="\U00002795\U0000FE0F Prompt",
+                        value=init_config["prompt"][0],
+                        lines=4,
+                        elem_id="prompt_box",
+                        show_copy_button=True,
+                    )
+                    negative_prompt = gr.Textbox(
+                        label="\U00002796\U0000FE0F Negative Prompt",
+                        value=init_config["negative_prompt"][0],
+                        lines=4,
+                        elem_id="negative_prompt_box",
+                        show_copy_button=True,
+                    )
+                with gr.Accordion(
+                    label="\U0001F4D0\U0000FE0F Advanced Settings", open=False
+                ):
+                    with gr.Accordion(label="Device Settings", open=False):
+                        device = gr.Dropdown(
+                            elem_id="device",
+                            label="Device",
+                            value=(
+                                init_config["device"]
+                                if init_config["device"]
+                                else "rocm"
+                            ),
+                            choices=global_obj.get_device_list(),
+                            allow_custom_value=True,
+                        )
+                        target_triple = gr.Textbox(
+                            elem_id="target_triple",
+                            label="Architecture",
+                            value=init_config["target_triple"],
+                        )
+                        with gr.Row():
+                            ondemand = gr.Checkbox(
+                                value=init_config["ondemand"],
+                                label="Low VRAM",
+                                interactive=True,
+                                visible=False,
+                            )
+                            precision = gr.Radio(
+                                label="Precision",
+                                value=init_config["precision"],
+                                choices=[
+                                    "fp16",
+                                    "fp32",
+                                ],
+                                visible=False,
+                            )
+                    with gr.Row():
+                        height = gr.Slider(
+                            512,
+                            1024,
+                            value=512,
+                            step=512,
+                            label="\U00002195\U0000FE0F Height",
+                            interactive=False,  # DEMO
+                            visible=False,  # DEMO
+                        )
+                        width = gr.Slider(
+                            512,
+                            1024,
+                            value=512,
+                            step=512,
+                            label="\U00002194\U0000FE0F Width",
+                            interactive=False,  # DEMO
+                            visible=False,  # DEMO
+                        )
+
+                    with gr.Accordion(
+                        label="\U0001F9EA\U0000FE0F Input Image Processing",
+                        open=False,
+                        visible=False,
+                    ):
+                        strength = gr.Slider(
+                            0,
+                            1,
+                            value=init_config["strength"],
+                            step=0.01,
+                            label="Denoising Strength",
+                        )
+                        resample_type = gr.Dropdown(
+                            value=init_config["resample_type"],
+                            choices=resampler_list,
+                            label="Resample Type",
+                            allow_custom_value=True,
+                        )
+                    with gr.Row():
+                        sd_model_info = (
+                            f"Checkpoint Path: {str(get_checkpoints_path())}"
+                        )
+                        base_model_id = gr.Dropdown(
+                            label="\U000026F0\U0000FE0F Base Model",
+                            info="Select or enter HF model ID",
+                            elem_id="custom_model",
+                            value=init_config["base_model_id"],
+                            choices=sd_default_models,
+                            allow_custom_value=True,
+                        )  # base_model_id
+                    with gr.Row(equal_height=True):
+                        seed = gr.Textbox(
+                            value=init_config["seed"],
+                            label="\U0001F331\U0000FE0F Seed",
+                            info="An integer, -1 for random",
+                            show_copy_button=True,
+                        )
+                        scheduler = gr.Dropdown(
+                            elem_id="scheduler",
+                            label="\U0001F4C5\U0000FE0F Scheduler",
+                            info="\U000E0020",  # forces same height as seed
+                            value=init_config["scheduler"],
+                            choices=scheduler_model_map.keys(),
+                            allow_custom_value=False,
+                            visible=False,
+                        )
+                    with gr.Row():
+                        steps = gr.Dropdown(
+                            value=20,
+                            choices=[10, 15, 20],
+                            label="\U0001F3C3\U0000FE0F Steps",
+                            allow_custom_value=True,
+                        )
+                        guidance_scale = gr.Slider(
+                            0,
+                            5,  # DEMO
+                            value=4,
+                            step=0.1,
+                            label="\U0001F5C3\U0000FE0F CFG Scale",
+                            visible=False,
+                        )
+                    with gr.Row():
+                        batch_count = gr.Slider(
+                            1,
+                            100,
+                            value=init_config["batch_count"],
+                            step=1,
+                            label="Batch Count",
+                            interactive=True,
+                            visible=False,
+                        )
+                        batch_size = gr.Slider(
+                            1,
+                            4,
+                            value=init_config["batch_size"],
+                            step=1,
+                            label="Batch Size",
+                            interactive=False,  # DEMO
+                            visible=False,
+                        )
+                        compiled_pipeline = gr.Checkbox(
+                            value=init_config["compiled_pipeline"],
+                            label="Faster txt2img (SDXL only)",
+                            visible=False,  # DEMO
+                        )
+                    with gr.Row(elem_classes=["fill"], visible=False):
+                        Path(get_configs_path()).mkdir(parents=True, exist_ok=True)
+                        write_default_sd_configs(get_configs_path())
+                        default_config_file = global_obj.get_init_config()
+                        sd_json = gr.JSON(
+                            elem_classes=["fill"],
+                            value=view_json_file(default_config_file),
+                        )
+                    with gr.Row(visible=False):
+                        with gr.Row():
+                            load_sd_config = gr.Dropdown(
+                                label="Load Config",
+                                value=cmd_opts.defaults,
+                                choices=get_configs(),
+                                allow_custom_value=True,
+                                visible=False,
+                            )
+                        with gr.Row():
+                            save_sd_config = gr.Button(value="Save Config", size="sm")
+                            clear_sd_config = gr.ClearButton(
+                                value="Clear Config",
+                                size="sm",
+                                components=sd_json,
+                            )
+                            # with gr.Row():
+                            sd_config_name = gr.Textbox(
+                                value="Config Name",
+                                info="Name of the file this config will be saved to.",
+                                interactive=True,
+                                show_label=False,
+                            )
+                with gr.Accordion(
+                    label="\U00002696\U0000FE0F Model Weights",
+                    open=False,
+                    visible=False,  # DEMO
+                ):
+                    with gr.Column():
+                        custom_weights = gr.Dropdown(
+                            label="Checkpoint Weights",
+                            info="Select or enter HF model ID",
+                            elem_id="custom_model",
+                            value=init_config["custom_weights"],
+                            allow_custom_value=True,
+                            choices=["None"]
+                            + get_checkpoints(os.path.basename(str(base_model_id))),
+                        )  # custom_weights
+                        sd_vae_info = (str(get_checkpoints_path("vae"))).replace(
+                            "\\", "\n\\"
+                        )
+                        sd_vae_info = f"VAE Path: {sd_vae_info}"
+                        custom_vae = gr.Dropdown(
+                            label=f"VAE Model",
+                            info=sd_vae_info,
+                            elem_id="custom_model",
+                            value=init_config["custom_vae"],
+                            choices=["None"] + get_checkpoints("vae"),
+                            allow_custom_value=True,
+                            scale=1,
+                        )
+                        sd_lora_info = (str(get_checkpoints_path("loras"))).replace(
+                            "\\", "\n\\"
+                        )
+                        lora_opt = gr.Dropdown(
+                            allow_custom_value=True,
+                            label=f"Standalone LoRA Weights",
+                            info=sd_lora_info,
+                            elem_id="lora_weights",
+                            value=(
+                                init_config["embeddings"][0]
+                                if (len(init_config["embeddings"].keys()) > 1)
+                                else "None"
+                            ),
+                            multiselect=True,
+                            choices=[] + get_checkpoints("lora"),
+                            scale=2,
+                        )
+                        lora_tags = gr.HTML(
+                            value="<div><i>No LoRA selected</i></div>",
+                            elem_classes="lora-tags",
+                        )
+                        embeddings_config = gr.JSON(
+                            label="Embeddings Options", min_width=50, scale=1
+                        )
+                        gr.on(
+                            triggers=[lora_opt.change],
+                            fn=lora_changed,
+                            inputs=[lora_opt],
+                            outputs=[lora_tags],
+                            queue=True,
+                            show_progress=False,
+                        ).then(
+                            fn=update_embeddings_json,
+                            inputs=[lora_opt],
+                            outputs=[embeddings_config],
+                            show_progress=False,
+                        )
+                with gr.Accordion(
+                    label="Controlnet Options",
+                    open=False,
+                    visible=False,
+                ):
+                    preprocessed_hints = gr.State([])
+                    with gr.Column():
+                        sd_cnet_info = (
+                            str(get_checkpoints_path("controlnet"))
+                        ).replace("\\", "\n\\")
+                    with gr.Row():
+                        cnet_config = gr.JSON()
+                        with gr.Column():
+                            clear_config = gr.ClearButton(
+                                value="Clear Controlnet Config",
+                                size="sm",
+                                components=cnet_config,
+                            )
+                            control_mode = gr.Radio(
+                                choices=["Prompt", "Balanced", "Controlnet"],
+                                value="Balanced",
+                                label="Control Mode",
+                            )
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            cnet_model = gr.Dropdown(
+                                allow_custom_value=True,
+                                label=f"Controlnet Model",
+                                info=sd_cnet_info,
+                                value="None",
+                                choices=[
+                                    "None",
+                                    "canny",
+                                    "openpose",
+                                    "scribble",
+                                    "zoedepth",
+                                ]
+                                + get_checkpoints("controlnet"),
+                            )
+                            cnet_strength = gr.Slider(
+                                label="Controlnet Strength",
+                                minimum=0,
+                                maximum=100,
+                                value=50,
+                                step=1,
+                            )
+                            with gr.Row():
+                                canvas_width = gr.Slider(
+                                    label="Canvas Width",
+                                    minimum=512,
+                                    maximum=1024,
+                                    value=512,
+                                    step=512,
+                                )
+                                canvas_height = gr.Slider(
+                                    label="Canvas Height",
+                                    minimum=512,
+                                    maximum=1024,
+                                    value=512,
+                                    step=512,
+                                )
+                            make_canvas = gr.Button(
+                                value="Make Canvas!",
+                            )
+                            use_input_img = gr.Button(
+                                value="Use Original Image",
+                                size="sm",
+                            )
+                        cnet_input = gr.Image(
+                            value=None,
+                            type="pil",
+                            image_mode="RGB",
+                            interactive=True,
+                        )
+                        with gr.Column(scale=1):
+                            cnet_output = gr.Image(
+                                value=None,
+                                visible=True,
+                                label="Preprocessed Hint",
+                                interactive=False,
+                                show_label=True,
+                            )
+                            cnet_gen = gr.Button(
+                                value="Preprocess controlnet input",
+                            )
+                            use_result = gr.Button(
+                                "Submit",
+                                size="sm",
+                            )
+                        make_canvas.click(
+                            fn=create_canvas,
+                            inputs=[canvas_width, canvas_height],
+                            outputs=[cnet_input],
+                            queue=False,
+                        )
+                        cnet_gen.click(
+                            fn=cnet_preview,
+                            inputs=[
+                                cnet_model,
+                                cnet_input,
+                            ],
+                            outputs=[
+                                cnet_output,
+                                preprocessed_hints,
+                            ],
+                        )
+                        use_result.click(
+                            fn=submit_to_cnet_config,
+                            inputs=[
+                                cnet_model,
+                                cnet_output,
+                                cnet_strength,
+                                control_mode,
+                                cnet_config,
+                            ],
+                            outputs=[
+                                cnet_config,
+                            ],
+                            queue=False,
+                        )
+            with gr.Column(scale=3, min_width=600):
+                with gr.Tabs() as sd_tabs:
+                    sd_element.load(
+                        # Workaround for Gradio issue #7085
+                        # TODO: revert to setting selected= in gr.Tabs declaration
+                        # once this is resolved in Gradio
+                        lambda: gr.Tabs(selected=101),
+                        outputs=[sd_tabs],
+                    )
+                    with gr.Tab(
+                        label="Input Image", id=100, visible=False
+                    ) as sd_tab_init_image:  # DEMO
+                        with gr.Column(elem_classes=["sd-right-panel"]):
+                            with gr.Row(elem_classes=["fill"]):
+                                # TODO: make this import image prompt info if it exists
+                                sd_init_image = gr.Image(
+                                    type="pil",
+                                    interactive=True,
+                                    show_label=False,
+                                )
+                                use_input_img.click(
+                                    fn=import_original,
+                                    inputs=[
+                                        sd_init_image,
+                                        canvas_width,
+                                        canvas_height,
+                                    ],
+                                    outputs=[cnet_input],
+                                    queue=False,
+                                )
+                    with gr.Tab(label="Generate Images", id=101) as sd_tab_gallery:
+                        with gr.Column(elem_classes=["sd-right-panel"]):
+                            with gr.Row(elem_classes=["fill"]):
+                                sd_gallery = gr.Gallery(
+                                    label="Generated images",
+                                    show_label=False,
+                                    elem_id="gallery",
+                                    columns=2,
+                                    object_fit="fit",
+                                    preview=True,
+                                )
+                            with gr.Row():
+                                stable_diffusion = gr.Button("Start")
+                                unload = gr.Button("Unload Models")
+                                unload.click(
+                                    fn=unload_sd,
+                                    queue=False,
+                                    show_progress=False,
+                                )
+                                stop_batch = gr.Button("Stop", visible=False)
+                    # with gr.Tab(label="Config", id=102) as sd_tab_config:
+                    #     with gr.Group():#elem_classes=["sd-right-panel"]):
+                    #         with gr.Row(elem_classes=["fill"], visible=False):
+                    #             Path(get_configs_path()).mkdir(
+                    #                 parents=True, exist_ok=True
+                    #             )
+                    #             write_default_sd_configs(get_configs_path())
+                    #             default_config_file = global_obj.get_init_config()
+                    #             sd_json = gr.JSON(
+                    #                 elem_classes=["fill"],
+                    #                 value=view_json_file(default_config_file),
+                    #             )
+                    #         with gr.Row():
+                    #             with gr.Row():
+                    #                 load_sd_config = gr.Dropdown(
+                    #                     label="Load Config",
+                    #                     value=cmd_opts.defaults,
+                    #                     choices=get_configs(),
+                    #                     allow_custom_value=True,
+                    #                 )
+                    #             with gr.Row():
+                    #                 save_sd_config = gr.Button(
+                    #                     value="Save Config", size="sm"
+                    #                 )
+                    #                 clear_sd_config = gr.ClearButton(
+                    #                     value="Clear Config",
+                    #                     size="sm",
+                    #                     components=sd_json,
+                    #                 )
+                    #                 # with gr.Row():
+                    #                 sd_config_name = gr.Textbox(
+                    #                     value="Config Name",
+                    #                     info="Name of the file this config will be saved to.",
+                    #                     interactive=True,
+                    #                     show_label=False,
+                    #                 )
+                    with gr.Tab(label="Log", id=103, visible=False) as sd_tab_log:
+                        with gr.Row():
+                            std_output = gr.Textbox(
+                                value=f"{sd_model_info}\n"
+                                f"Images will be saved at "
+                                f"{get_generated_imgs_path()}",
+                                lines=2,
+                                elem_id="std_output",
+                                show_label=True,
+                                label="Log",
+                                show_copy_button=True,
+                            )
+                            sd_element.load(
+                                logger.read_sd_logs, None, std_output, every=1
+                            )
+                            sd_status = gr.Textbox(visible=False)
+    base_model_id.change(
+        fn=base_model_changed,
+        inputs=[base_model_id],
+        outputs=[custom_weights, steps],
+    )
+    load_sd_config.change(
+        fn=load_sd_cfg,
+        inputs=[sd_json, load_sd_config],
+        outputs=[
+            prompt,
+            negative_prompt,
+            sd_init_image,
+            height,
+            width,
+            steps,
+            strength,
+            guidance_scale,
+            seed,
+            batch_count,
+            batch_size,
+            scheduler,
+            base_model_id,
+            custom_weights,
+            custom_vae,
+            precision,
+            device,
+            target_triple,
+            ondemand,
+            compiled_pipeline,
+            resample_type,
+            cnet_config,
+            embeddings_config,
+            sd_json,
+        ],
+    )
+    save_sd_config.click(
+        fn=save_sd_cfg,
+        inputs=[sd_json, sd_config_name],
+        outputs=[sd_config_name],
+    )
+    pull_kwargs = dict(
+        fn=pull_sd_configs,
+        inputs=[
+            prompt,
+            negative_prompt,
+            sd_init_image,
+            height,
+            width,
+            steps,
+            strength,
+            guidance_scale,
+            seed,
+            batch_count,
+            batch_size,
+            scheduler,
+            base_model_id,
+            custom_weights,
+            custom_vae,
+            precision,
+            device,
+            target_triple,
+            ondemand,
+            compiled_pipeline,
+            resample_type,
+            cnet_config,
+            embeddings_config,
+        ],
+        outputs=[
+            sd_json,
+        ],
+    )
+
+    status_kwargs = dict(
+        fn=lambda bc, bs: status_label("Stable Diffusion", 0, bc, bs),
+        inputs=[batch_count, batch_size],
+        outputs=sd_status,
+    )
+
+    gen_kwargs = dict(
+        fn=shark_sd_fn_dict_input,
+        inputs=[sd_json],
+        outputs=[
+            sd_gallery,
+            sd_status,
+        ],
+    )
+
+    prompt_submit = prompt.submit(**status_kwargs).then(**pull_kwargs)
+    neg_prompt_submit = negative_prompt.submit(**status_kwargs).then(**pull_kwargs)
+    generate_click = (
+        stable_diffusion.click(**status_kwargs).then(**pull_kwargs).then(**gen_kwargs)
+    )
+    stop_batch.click(
+        fn=cancel_sd,
+        cancels=[prompt_submit, neg_prompt_submit, generate_click],
+    )

apps/shark_studio/web/ui/utils.py

@@ -0,0 +1,43 @@
+from enum import IntEnum
+import math
+import sys
+import os
+
+
+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)
+
+
+amdlogo_loc = resource_path("logos/amd-logo.jpg")
+amdicon_loc = resource_path("logos/amd-icon.jpg")
+
+
+class HSLHue(IntEnum):
+    RED = 0
+    YELLOW = 60
+    GREEN = 120
+    CYAN = 180
+    BLUE = 240
+    MAGENTA = 300
+
+
+def hsl_color(alpha: float, start, end):
+    b = (end - start) * (alpha if alpha > 0 else 0)
+    result = b + start
+
+    # Return a CSS HSL string
+    return f"hsl({math.floor(result)}, 80%, 35%)"
+
+
+def none_to_str_none(props: dict):
+    for key in props:
+        props[key] = "None" if props[key] == None else props[key]
+    return props
+
+
+def str_none_to_none(props: dict):
+    for key in props:
+        props[key] = None if props[key] == "None" else props[key]
+    return props

apps/shark_studio/web/utils.py

@@ -0,0 +1,12 @@
+import os
+import sys
+
+
+def get_available_devices():
+    return ["cpu-task"]
+
+
+def get_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)

apps/shark_studio/web/utils/default_configs.py

@@ -0,0 +1,95 @@
+default_sd_config = r"""{
+  "prompt": [
+    "a photo taken of the front of a super-car drifting on a road near mountains at high speeds with smoke coming off the tires, front angle, front point of view, trees in the mountains of the background, ((sharp focus))"
+  ],
+  "negative_prompt": [
+    "watermark, signature, logo, text, lowres, ((monochrome, grayscale)), blurry, ugly, blur, oversaturated, cropped"
+  ],
+  "sd_init_image": [null],
+  "height": 512,
+  "width": 512,
+  "steps": 50,
+  "strength": 0.8,
+  "guidance_scale": 7.5,
+  "seed": "-1",
+  "batch_count": 1,
+  "batch_size": 1,
+  "scheduler": "EulerDiscrete",
+  "base_model_id": "stabilityai/stable-diffusion-2-1-base",
+  "custom_weights": null,
+  "custom_vae": null,
+  "precision": "fp16",
+  "device": "",
+  "target_triple": "",
+  "ondemand": false,
+  "compiled_pipeline": false,
+  "resample_type": "Nearest Neighbor",
+  "controlnets": {},
+  "embeddings": {}
+}"""
+
+sdxl_30steps = r"""{
+  "prompt": [
+    "a cat under the snow with blue eyes, covered by snow, cinematic style, medium shot, professional photo, animal"
+  ],
+  "negative_prompt": [
+    "watermark, signature, logo, text, lowres, ((monochrome, grayscale)), blurry, ugly, blur, oversaturated, cropped"
+  ],
+  "sd_init_image": [null],
+  "height": 1024,
+  "width": 1024,
+  "steps": 30,
+  "strength": 0.8,
+  "guidance_scale": 7.5,
+  "seed": "-1",
+  "batch_count": 1,
+  "batch_size": 1,
+  "scheduler": "EulerDiscrete",
+  "base_model_id": "stabilityai/stable-diffusion-xl-base-1.0",
+  "custom_weights": null,
+  "custom_vae": null,
+  "precision": "fp16",
+  "device": "",
+  "target_triple": "",
+  "ondemand": false,
+  "compiled_pipeline": true,
+  "resample_type": "Nearest Neighbor",
+  "controlnets": {},
+  "embeddings": {}
+}"""
+
+sdxl_turbo = r"""{
+  "prompt": [
+    "A cat wearing a hat that says 'TURBO' on it. The cat is sitting on a skateboard."
+  ],
+  "negative_prompt": [
+    ""
+  ],
+  "sd_init_image": [null],
+  "height": 512,
+  "width": 512,
+  "steps": 2,
+  "strength": 0.8,
+  "guidance_scale": 0,
+  "seed": "-1",
+  "batch_count": 1,
+  "batch_size": 1,
+  "scheduler": "EulerAncestralDiscrete",
+  "base_model_id": "stabilityai/sdxl-turbo",
+  "custom_weights": null,
+  "custom_vae": null,
+  "precision": "fp16",
+  "device": "",
+  "target_triple": "",
+  "ondemand": false,
+  "compiled_pipeline": true,
+  "resample_type": "Nearest Neighbor",
+  "controlnets": {},
+  "embeddings": {}
+}"""
+
+default_sd_configs = {
+    # "default_sd_config.json": sdxl_turbo,
+    # "sdxl-30steps.json": sdxl_30steps,
+    "sdxl-turbo.json": sdxl_turbo,
+}

apps/shark_studio/web/utils/file_utils.py

@@ -0,0 +1,115 @@
+import os
+import sys
+import glob
+from datetime import datetime as dt
+from pathlib import Path
+
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+
+checkpoints_filetypes = (
+    "*.ckpt",
+    "*.safetensors",
+)
+
+from apps.shark_studio.web.utils.default_configs import default_sd_configs
+
+
+def write_default_sd_configs(path):
+    for key in default_sd_configs.keys():
+        config_fpath = os.path.join(path, key)
+        if not os.path.exists(config_fpath):
+            with open(config_fpath, "w") as f:
+                f.write(default_sd_configs[key])
+
+
+def safe_name(name):
+    return name.split("/")[-1].replace("-", "_")
+
+
+def get_path_stem(path):
+    path = Path(path)
+    return path.stem
+
+
+def get_resource_path(path):
+    """Get absolute path to resource, works for dev and for PyInstaller"""
+    if os.path.isabs(path):
+        return path
+    else:
+        base_path = getattr(sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__)))
+        result = Path(os.path.join(base_path, path)).resolve(strict=False)
+        return result
+
+
+def get_configs_path() -> Path:
+    configs = get_resource_path(cmd_opts.config_dir)
+    if not os.path.exists(configs):
+        os.mkdir(configs)
+    return Path(configs)
+
+
+def get_generated_imgs_path() -> Path:
+    outputs = get_resource_path(cmd_opts.output_dir)
+    if not os.path.exists(outputs):
+        os.mkdir(outputs)
+    return Path(outputs)
+
+
+def get_tmp_path() -> Path:
+    tmpdir = get_resource_path(cmd_opts.model_dir)
+    if not os.path.exists(tmpdir):
+        os.mkdir(tmpdir)
+    return Path(tmpdir)
+
+
+def get_generated_imgs_todays_subdir() -> str:
+    return dt.now().strftime("%Y%m%d")
+
+
+def create_model_folders():
+    dir = ["checkpoints", "vae", "lora", "vmfb"]
+    if not os.path.isdir(cmd_opts.model_dir):
+        try:
+            os.makedirs(cmd_opts.model_dir)
+        except OSError:
+            sys.exit(
+                f"Invalid --model_dir argument, "
+                f"{cmd_opts.model_dir} folder does not exist, and cannot be created."
+            )
+
+    for root in dir:
+        Path(get_checkpoints_path(root)).mkdir(parents=True, exist_ok=True)
+
+
+def get_checkpoints_path(model_type=""):
+    return get_resource_path(os.path.join(cmd_opts.model_dir, model_type))
+
+
+def get_checkpoints(model_type="checkpoints"):
+    ckpt_files = []
+    file_types = checkpoints_filetypes
+    if model_type == "scripts":
+        file_types = ["shark_*.py"]
+    if model_type == "lora":
+        file_types = file_types + ("*.pt", "*.bin")
+    for extn in file_types:
+        files = [
+            os.path.basename(x)
+            for x in glob.glob(os.path.join(get_checkpoints_path(model_type), extn))
+        ]
+    ckpt_files.extend(files)
+    return sorted(ckpt_files, key=str.casefold)
+
+
+def get_configs():
+    return sorted(
+        [
+            os.path.basename(x)
+            for x in glob.glob(os.path.join(get_configs_path(), "*.json"))
+        ],
+        key=str.casefold,
+    )
+
+
+def get_checkpoint_pathfile(checkpoint_name, model_type="checkpoints"):
+    return os.path.join(get_checkpoints_path(model_type), checkpoint_name)

apps/shark_studio/web/utils/globals.py

@@ -0,0 +1,158 @@
+import gc
+from ...api.utils import get_available_devices
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+import os
+from apps.shark_studio.web.utils.file_utils import get_configs_path
+
+"""
+The global objects include SD pipeline and config.
+Maintaining the global objects would avoid creating extra pipeline objects when switching modes.
+Also we could avoid memory leak when switching models by clearing the cache.
+"""
+
+
+def view_json_file(file_path):
+    content = ""
+    with open(file_path, "r") as fopen:
+        content = fopen.read()
+    return content
+
+
+def _init():
+    global _sd_obj
+    global _llm_obj
+    global _devices
+    global _pipe_kwargs
+    global _prep_kwargs
+    global _gen_kwargs
+    global _schedulers
+    _sd_obj = None
+    _llm_obj = None
+    _devices = None
+    _pipe_kwargs = None
+    _prep_kwargs = None
+    _gen_kwargs = None
+    _schedulers = None
+    set_devices()
+
+
+def set_sd_obj(value):
+    global _sd_obj
+    global _llm_obj
+    _llm_obj = None
+    _sd_obj = value
+
+
+def set_llm_obj(value):
+    global _sd_obj
+    global _llm_obj
+    _llm_obj = value
+    _sd_obj = None
+
+
+def set_devices():
+    global _devices
+    _devices = get_available_devices()
+
+
+def set_sd_scheduler(key):
+    global _sd_obj
+    _sd_obj.scheduler = _schedulers[key]
+
+
+def set_sd_status(value):
+    global _sd_obj
+    _sd_obj.status = value
+
+
+def set_pipe_kwargs(value):
+    global _pipe_kwargs
+    _pipe_kwargs = value
+
+
+def set_prep_kwargs(value):
+    global _prep_kwargs
+    _prep_kwargs = value
+
+
+def set_gen_kwargs(value):
+    global _gen_kwargs
+    _gen_kwargs = value
+
+
+def set_schedulers(value):
+    global _schedulers
+    _schedulers = value
+
+
+def get_sd_obj():
+    global _sd_obj
+    return _sd_obj
+
+
+def get_llm_obj():
+    global _llm_obj
+    return _llm_obj
+
+
+def get_device_list():
+    global _devices
+    return _devices
+
+
+def get_init_config():
+    global _init_config
+    if os.path.exists(cmd_opts.defaults):
+        _init_config = cmd_opts.defaults
+    elif os.path.exists(os.path.join(get_configs_path(), cmd_opts.defaults)):
+        _init_config = os.path.join(get_configs_path(), cmd_opts.defaults)
+    else:
+        print(
+            "Default config not found as absolute path or in configs folder. Using sdxl-turbo as default config."
+        )
+        _init_config = os.path.join(get_configs_path(), "sdxl-turbo.json")
+    return _init_config
+
+
+def get_sd_status():
+    global _sd_obj
+    return _sd_obj.status
+
+
+def get_pipe_kwargs():
+    global _pipe_kwargs
+    return _pipe_kwargs
+
+
+def get_prep_kwargs():
+    global _prep_kwargs
+    return _prep_kwargs
+
+
+def get_gen_kwargs():
+    global _gen_kwargs
+    return _gen_kwargs
+
+
+def get_scheduler(key):
+    global _schedulers
+    return _schedulers[key]
+
+
+def clear_cache():
+    global _sd_obj
+    global _llm_obj
+    global _pipe_kwargs
+    global _prep_kwargs
+    global _gen_kwargs
+    global _schedulers
+    del _sd_obj
+    del _llm_obj
+    del _schedulers
+    gc.collect()
+    _sd_obj = None
+    _llm_obj = None
+    _pipe_kwargs = None
+    _prep_kwargs = None
+    _gen_kwargs = None
+    _schedulers = None

apps/shark_studio/web/utils/metadata/__init__.py

@@ -0,0 +1,6 @@
+from .png_metadata import (
+    import_png_metadata,
+)
+from .display import (
+    displayable_metadata,
+)

apps/shark_studio/web/utils/metadata/csv_metadata.py

@@ -0,0 +1,43 @@
+import csv
+import os
+from .format import humanize, humanizable
+
+
+def csv_path(image_filename: str):
+    return os.path.join(os.path.dirname(image_filename), "imgs_details.csv")
+
+
+def has_csv(image_filename: str) -> bool:
+    return os.path.exists(csv_path(image_filename))
+
+
+def matching_filename(image_filename: str, row):
+    # we assume the final column of the csv has the original filename with full path and match that
+    # against the image_filename if we are given a list. Otherwise we assume a dict and and take
+    # the value of the OUTPUT key
+    return os.path.basename(image_filename) in (
+        row[-1] if isinstance(row, list) else row["OUTPUT"]
+    )
+
+
+def parse_csv(image_filename: str):
+    csv_filename = csv_path(image_filename)
+
+    with open(csv_filename, "r", newline="") as csv_file:
+        # We use a reader or DictReader here for images_details.csv depending on whether we think it
+        # has headers or not. Having headers means less guessing of the format.
+        has_header = csv.Sniffer().has_header(csv_file.read(2048))
+        csv_file.seek(0)
+
+        reader = csv.DictReader(csv_file) if has_header else csv.reader(csv_file)
+
+        matches = [
+            # we rely on humanize and humanizable to work out the parsing of the individual .csv rows
+            humanize(row)
+            for row in reader
+            if row
+            and (has_header or humanizable(row))
+            and matching_filename(image_filename, row)
+        ]
+
+    return matches[0] if matches else {}

apps/shark_studio/web/utils/metadata/display.py

@@ -0,0 +1,53 @@
+import json
+import os
+from PIL import Image
+from .png_metadata import parse_generation_parameters
+from .exif_metadata import has_exif, parse_exif
+from .csv_metadata import has_csv, parse_csv
+from .format import compact, humanize
+
+
+def displayable_metadata(image_filename: str) -> dict:
+    if not os.path.isfile(image_filename):
+        return {"source": "missing", "parameters": {}}
+
+    pil_image = Image.open(image_filename)
+
+    # we have PNG generation parameters (preferred, as it's what the txt2img dropzone reads,
+    # and we go via that for SendTo, and is directly tied to the image)
+    if "parameters" in pil_image.info:
+        return {
+            "source": "png",
+            "parameters": compact(
+                parse_generation_parameters(pil_image.info["parameters"])
+            ),
+        }
+
+    # we have a matching json file (next most likely to be accurate when it's there)
+    json_path = os.path.splitext(image_filename)[0] + ".json"
+    if os.path.isfile(json_path):
+        with open(json_path) as params_file:
+            return {
+                "source": "json",
+                "parameters": compact(
+                    humanize(json.load(params_file), includes_filename=False)
+                ),
+            }
+
+    # we have a CSV file so try that (can be different shapes, and it usually has no
+    # headers/param names so of the things we we *know* have parameters, it's the
+    # last resort)
+    if has_csv(image_filename):
+        params = parse_csv(image_filename)
+        if params:  # we might not have found the filename in the csv
+            return {
+                "source": "csv",
+                "parameters": compact(params),  # already humanized
+            }
+
+    # EXIF data, probably a .jpeg, may well not include parameters, but at least it's *something*
+    if has_exif(image_filename):
+        return {"source": "exif", "parameters": parse_exif(pil_image)}
+
+    # we've got nothing
+    return None

apps/shark_studio/web/utils/metadata/exif_metadata.py

@@ -0,0 +1,52 @@
+from PIL import Image
+from PIL.ExifTags import Base as EXIFKeys, TAGS, IFD, GPSTAGS
+
+
+def has_exif(image_filename: str) -> bool:
+    return True if Image.open(image_filename).getexif() else False
+
+
+def parse_exif(pil_image: Image) -> dict:
+    img_exif = pil_image.getexif()
+
+    # See this stackoverflow answer for where most this comes from: https://stackoverflow.com/a/75357594
+    # I did try to use the exif library but it broke just as much as my initial attempt at this (albeit I
+    # I was probably using it wrong) so I reverted back to using PIL with more filtering and saved a
+    # dependency
+    exif_tags = {
+        TAGS.get(key, key): str(val)
+        for (key, val) in img_exif.items()
+        if key in TAGS
+        and key not in (EXIFKeys.ExifOffset, EXIFKeys.GPSInfo)
+        and val
+        and (not isinstance(val, bytes))
+        and (not str(val).isspace())
+    }
+
+    def try_get_ifd(ifd_id):
+        try:
+            return img_exif.get_ifd(ifd_id).items()
+        except KeyError:
+            return {}
+
+    ifd_tags = {
+        TAGS.get(key, key): str(val)
+        for ifd_id in IFD
+        for (key, val) in try_get_ifd(ifd_id)
+        if ifd_id != IFD.GPSInfo
+        and key in TAGS
+        and val
+        and (not isinstance(val, bytes))
+        and (not str(val).isspace())
+    }
+
+    gps_tags = {
+        GPSTAGS.get(key, key): str(val)
+        for (key, val) in try_get_ifd(IFD.GPSInfo)
+        if key in GPSTAGS
+        and val
+        and (not isinstance(val, bytes))
+        and (not str(val).isspace())
+    }
+
+    return {**exif_tags, **ifd_tags, **gps_tags}

apps/shark_studio/web/utils/metadata/format.py

@@ -0,0 +1,139 @@
+# As SHARK has evolved more columns have been added to images_details.csv. However, since
+# no version of the CSV has any headers (yet) we don't actually have anything within the
+# file that tells us which parameter each column is for. So this is a list of known patterns
+# indexed by length which is what we're going to have to use to guess which columns are the
+# right ones for the file we're looking at.
+
+# The same ordering is used for JSON, but these do have key names, however they are not very
+# human friendly, nor do they match up with the what is written to the .png headers
+
+# So these are functions to try and get something consistent out the raw input from all
+# these sources
+
+PARAMS_FORMATS = {
+    9: {
+        "VARIANT": "Model",
+        "SCHEDULER": "Sampler",
+        "PROMPT": "Prompt",
+        "NEG_PROMPT": "Negative prompt",
+        "SEED": "Seed",
+        "CFG_SCALE": "CFG scale",
+        "PRECISION": "Precision",
+        "STEPS": "Steps",
+        "OUTPUT": "Filename",
+    },
+    10: {
+        "MODEL": "Model",
+        "VARIANT": "Variant",
+        "SCHEDULER": "Sampler",
+        "PROMPT": "Prompt",
+        "NEG_PROMPT": "Negative prompt",
+        "SEED": "Seed",
+        "CFG_SCALE": "CFG scale",
+        "PRECISION": "Precision",
+        "STEPS": "Steps",
+        "OUTPUT": "Filename",
+    },
+    12: {
+        "VARIANT": "Model",
+        "SCHEDULER": "Sampler",
+        "PROMPT": "Prompt",
+        "NEG_PROMPT": "Negative prompt",
+        "SEED": "Seed",
+        "CFG_SCALE": "CFG scale",
+        "PRECISION": "Precision",
+        "STEPS": "Steps",
+        "HEIGHT": "Height",
+        "WIDTH": "Width",
+        "MAX_LENGTH": "Max Length",
+        "OUTPUT": "Filename",
+    },
+}
+
+PARAMS_FORMAT_CURRENT = {
+    "VARIANT": "Model",
+    "VAE": "VAE",
+    "LORA": "LoRA",
+    "SCHEDULER": "Sampler",
+    "PROMPT": "Prompt",
+    "NEG_PROMPT": "Negative prompt",
+    "SEED": "Seed",
+    "CFG_SCALE": "CFG scale",
+    "PRECISION": "Precision",
+    "STEPS": "Steps",
+    "HEIGHT": "Height",
+    "WIDTH": "Width",
+    "MAX_LENGTH": "Max Length",
+    "OUTPUT": "Filename",
+}
+
+
+def compact(metadata: dict) -> dict:
+    # we don't want to alter the original dictionary
+    result = dict(metadata)
+
+    # discard the filename because we should already have it
+    if result.keys() & {"Filename"}:
+        result.pop("Filename")
+
+    # make showing the sizes more compact by using only one line each
+    if result.keys() & {"Size-1", "Size-2"}:
+        result["Size"] = f"{result.pop('Size-1')}x{result.pop('Size-2')}"
+    elif result.keys() & {"Height", "Width"}:
+        result["Size"] = f"{result.pop('Height')}x{result.pop('Width')}"
+
+    if result.keys() & {"Hires resize-1", "Hires resize-1"}:
+        hires_y = result.pop("Hires resize-1")
+        hires_x = result.pop("Hires resize-2")
+
+        if hires_x == 0 and hires_y == 0:
+            result["Hires resize"] = "None"
+        else:
+            result["Hires resize"] = f"{hires_y}x{hires_x}"
+
+    # remove VAE if it exists and is empty
+    if (result.keys() & {"VAE"}) and (not result["VAE"] or result["VAE"] == "None"):
+        result.pop("VAE")
+
+    # remove LoRA if it exists and is empty
+    if (result.keys() & {"LoRA"}) and (not result["LoRA"] or result["LoRA"] == "None"):
+        result.pop("LoRA")
+
+    return result
+
+
+def humanizable(metadata: dict | list[str], includes_filename=True) -> dict:
+    lookup_key = len(metadata) + (0 if includes_filename else 1)
+    return lookup_key in PARAMS_FORMATS.keys()
+
+
+def humanize(metadata: dict | list[str], includes_filename=True) -> dict:
+    lookup_key = len(metadata) + (0 if includes_filename else 1)
+
+    # For lists we can only work based on the length, we have no other information
+    if isinstance(metadata, list):
+        if humanizable(metadata, includes_filename):
+            return dict(zip(PARAMS_FORMATS[lookup_key].values(), metadata))
+        else:
+            raise KeyError(
+                f"Humanize could not find the format for a parameter list of length {len(metadata)}"
+            )
+
+    # For dictionaries we try to use the matching length parameter format if
+    # available, otherwise we just use the current format which is assumed to
+    # have everything currently known about. Then we swap keys in the metadata
+    # that match keys in the format for the friendlier name that we have set
+    # in the format value
+    if isinstance(metadata, dict):
+        if humanizable(metadata, includes_filename):
+            format = PARAMS_FORMATS[lookup_key]
+        else:
+            format = PARAMS_FORMAT_CURRENT
+
+        return {
+            format[key]: metadata[key]
+            for key in format.keys()
+            if key in metadata.keys() and metadata[key]
+        }
+
+    raise TypeError("Can only humanize parameter lists or dictionaries")

apps/shark_studio/web/utils/metadata/png_metadata.py

@@ -0,0 +1,216 @@
+import re
+from pathlib import Path
+from apps.shark_studio.web.utils.file_utils import (
+    get_checkpoint_pathfile,
+)
+from apps.shark_studio.api.sd import EMPTY_SD_MAP as sd_model_map
+
+from apps.shark_studio.modules.schedulers import (
+    scheduler_model_map,
+)
+
+re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
+re_param = re.compile(re_param_code)
+re_imagesize = re.compile(r"^(\d+)x(\d+)$")
+
+
+def parse_generation_parameters(x: str):
+    res = {}
+    prompt = ""
+    negative_prompt = ""
+    done_with_prompt = False
+
+    *lines, lastline = x.strip().split("\n")
+    if len(re_param.findall(lastline)) < 3:
+        lines.append(lastline)
+        lastline = ""
+
+    for i, line in enumerate(lines):
+        line = line.strip()
+        if line.startswith("Negative prompt:"):
+            done_with_prompt = True
+            line = line[16:].strip()
+
+        if done_with_prompt:
+            negative_prompt += ("" if negative_prompt == "" else "\n") + line
+        else:
+            prompt += ("" if prompt == "" else "\n") + line
+
+    res["Prompt"] = prompt
+    res["Negative prompt"] = negative_prompt
+
+    for k, v in re_param.findall(lastline):
+        v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
+        m = re_imagesize.match(v)
+        if m is not None:
+            res[k + "-1"] = m.group(1)
+            res[k + "-2"] = m.group(2)
+        else:
+            res[k] = v
+
+    # Missing CLIP skip means it was set to 1 (the default)
+    if "Clip skip" not in res:
+        res["Clip skip"] = "1"
+
+    hypernet = res.get("Hypernet", None)
+    if hypernet is not None:
+        res[
+            "Prompt"
+        ] += f"""<hypernet:{hypernet}:{res.get("Hypernet strength", "1.0")}>"""
+
+    if "Hires resize-1" not in res:
+        res["Hires resize-1"] = 0
+        res["Hires resize-2"] = 0
+
+    return res
+
+
+def try_find_model_base_from_png_metadata(file: str, folder: str = "models") -> str:
+    custom = ""
+
+    # Remove extension from file info
+    if file.endswith(".safetensors") or file.endswith(".ckpt"):
+        file = Path(file).stem
+    # Check for the file name match with one of the local ckpt or safetensors files
+    if Path(get_checkpoint_pathfile(file + ".ckpt", folder)).is_file():
+        custom = file + ".ckpt"
+    if Path(get_checkpoint_pathfile(file + ".safetensors", folder)).is_file():
+        custom = file + ".safetensors"
+
+    return custom
+
+
+def find_model_from_png_metadata(
+    key: str, metadata: dict[str, str | int]
+) -> tuple[str, str]:
+    png_hf_id = ""
+    png_custom = ""
+
+    if key in metadata:
+        model_file = metadata[key]
+        png_custom = try_find_model_base_from_png_metadata(model_file)
+        # Check for a model match with one of the default model list (ex: "Linaqruf/anything-v3.0")
+        if model_file in sd_model_map:
+            png_custom = model_file
+        # If nothing had matched, check vendor/hf_model_id
+        if not png_custom and model_file.count("/"):
+            png_hf_id = model_file
+        # No matching model was found
+        if not png_custom and not png_hf_id:
+            print(
+                "Import PNG info: Unable to find a matching model for %s" % model_file
+            )
+
+    return png_custom, png_hf_id
+
+
+def find_vae_from_png_metadata(key: str, metadata: dict[str, str | int]) -> str:
+    vae_custom = ""
+
+    if key in metadata:
+        vae_file = metadata[key]
+        vae_custom = try_find_model_base_from_png_metadata(vae_file, "vae")
+
+    # VAE input is optional, should not print or throw an error if missing
+
+    return vae_custom
+
+
+def find_lora_from_png_metadata(
+    key: str, metadata: dict[str, str | int]
+) -> tuple[str, str]:
+    lora_hf_id = ""
+    lora_custom = ""
+
+    if key in metadata:
+        lora_file = metadata[key]
+        lora_custom = try_find_model_base_from_png_metadata(lora_file, "lora")
+        # If nothing had matched, check vendor/hf_model_id
+        if not lora_custom and lora_file.count("/"):
+            lora_hf_id = lora_file
+
+    # LoRA input is optional, should not print or throw an error if missing
+
+    return lora_custom, lora_hf_id
+
+
+def import_png_metadata(
+    pil_data,
+    prompt,
+    negative_prompt,
+    steps,
+    sampler,
+    cfg_scale,
+    seed,
+    width,
+    height,
+    custom_model,
+    custom_lora,
+    hf_lora_id,
+    custom_vae,
+):
+    try:
+        png_info = pil_data.info["parameters"]
+        metadata = parse_generation_parameters(png_info)
+
+        (png_custom_model, png_hf_model_id) = find_model_from_png_metadata(
+            "Model", metadata
+        )
+        (lora_custom_model, lora_hf_model_id) = find_lora_from_png_metadata(
+            "LoRA", metadata
+        )
+        vae_custom_model = find_vae_from_png_metadata("VAE", metadata)
+
+        negative_prompt = metadata["Negative prompt"]
+        steps = int(metadata["Steps"])
+        cfg_scale = float(metadata["CFG scale"])
+        seed = int(metadata["Seed"])
+        width = float(metadata["Size-1"])
+        height = float(metadata["Size-2"])
+
+        if "Model" in metadata and png_custom_model:
+            custom_model = png_custom_model
+        elif "Model" in metadata and png_hf_model_id:
+            custom_model = png_hf_model_id
+
+        if "LoRA" in metadata and lora_custom_model:
+            custom_lora = lora_custom_model
+            hf_lora_id = ""
+        if "LoRA" in metadata and lora_hf_model_id:
+            custom_lora = "None"
+            hf_lora_id = lora_hf_model_id
+
+        if "VAE" in metadata and vae_custom_model:
+            custom_vae = vae_custom_model
+
+        if "Prompt" in metadata:
+            prompt = metadata["Prompt"]
+        if "Sampler" in metadata:
+            if metadata["Sampler"] in scheduler_model_map:
+                sampler = metadata["Sampler"]
+            else:
+                print(
+                    "Import PNG info: Unable to find a scheduler for %s"
+                    % metadata["Sampler"]
+                )
+
+    except Exception as ex:
+        if pil_data and pil_data.info.get("parameters"):
+            print("import_png_metadata failed with %s" % ex)
+        pass
+
+    return (
+        None,
+        prompt,
+        negative_prompt,
+        steps,
+        sampler,
+        cfg_scale,
+        seed,
+        width,
+        height,
+        custom_model,
+        custom_lora,
+        hf_lora_id,
+        custom_vae,
+    )

apps/shark_studio/web/utils/state.py

@@ -0,0 +1,39 @@
+import apps.shark_studio.web.utils.globals as global_obj
+import gc
+
+
+def status_label(tab_name, batch_index=0, batch_count=1, batch_size=1):
+    if batch_index < batch_count:
+        bs = f"x{batch_size}" if batch_size > 1 else ""
+        return f"{tab_name} generating {batch_index+1}/{batch_count}{bs}"
+    else:
+        return f"{tab_name} complete"
+
+
+def get_generation_text_info(seeds, device):
+    cfg_dump = {}
+    for cfg in global_obj.get_config_dict():
+        cfg_dump[cfg] = cfg
+    text_output = f"prompt={cfg_dump['prompts']}"
+    text_output += f"\nnegative prompt={cfg_dump['negative_prompts']}"
+    text_output += (
+        f"\nmodel_id={cfg_dump['hf_model_id']}, " f"ckpt_loc={cfg_dump['ckpt_loc']}"
+    )
+    text_output += f"\nscheduler={cfg_dump['scheduler']}, " f"device={device}"
+    text_output += (
+        f"\nsteps={cfg_dump['steps']}, "
+        f"guidance_scale={cfg_dump['guidance_scale']}, "
+        f"seed={seeds}"
+    )
+    text_output += (
+        f"\nsize={cfg_dump['height']}x{cfg_dump['width']}, "
+        if not cfg_dump.use_hiresfix
+        else f"\nsize={cfg_dump['hiresfix_height']}x{cfg_dump['hiresfix_width']}, "
+    )
+    text_output += (
+        f"batch_count={cfg_dump['batch_count']}, "
+        f"batch_size={cfg_dump['batch_size']}, "
+        f"max_length={cfg_dump['max_length']}"
+    )
+
+    return text_output

apps/shark_studio/web/utils/tmp_configs.py

@@ -0,0 +1,75 @@
+import os
+import shutil
+from time import time
+
+from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+
+shark_tmp = cmd_opts.tmp_dir  # os.path.join(os.getcwd(), "shark_tmp/")
+
+
+def clear_tmp_mlir():
+    cleanup_start = time()
+    print("Clearing .mlir temporary files from a prior run. This may take some time...")
+    mlir_files = [
+        filename
+        for filename in os.listdir(shark_tmp)
+        if os.path.isfile(os.path.join(shark_tmp, filename))
+        and filename.endswith(".mlir")
+    ]
+    for filename in mlir_files:
+        os.remove(os.path.join(shark_tmp, filename))
+    print(f"Clearing .mlir temporary files took {time() - cleanup_start:.4f} seconds.")
+
+
+def clear_tmp_imgs():
+    # tell gradio to use a directory under shark_tmp for its temporary
+    # image files unless somewhere else has been set
+    if "GRADIO_TEMP_DIR" not in os.environ:
+        os.environ["GRADIO_TEMP_DIR"] = os.path.join(shark_tmp, "gradio")
+
+    print(
+        f"gradio temporary image cache located at {os.environ['GRADIO_TEMP_DIR']}. "
+        + "You may change this by setting the GRADIO_TEMP_DIR environment variable."
+    )
+
+    # Clear all gradio tmp images from the last session
+    if os.path.exists(os.environ["GRADIO_TEMP_DIR"]):
+        cleanup_start = time()
+        print(
+            "Clearing gradio UI temporary image files from a prior run. This may take some time..."
+        )
+        shutil.rmtree(os.environ["GRADIO_TEMP_DIR"], ignore_errors=True)
+        print(
+            f"Clearing gradio UI temporary image files took {time() - cleanup_start:.4f} seconds."
+        )
+
+    # older SHARK versions had to workaround gradio bugs and stored things differently
+    else:
+        image_files = [
+            filename
+            for filename in os.listdir(shark_tmp)
+            if os.path.isfile(os.path.join(shark_tmp, filename))
+            and filename.startswith("tmp")
+            and filename.endswith(".png")
+        ]
+        if len(image_files) > 0:
+            print(
+                "Clearing temporary image files of a prior run of a previous SHARK version. This may take some time..."
+            )
+            cleanup_start = time()
+            for filename in image_files:
+                os.remove(shark_tmp + filename)
+            print(
+                f"Clearing temporary image files took {time() - cleanup_start:.4f} seconds."
+            )
+        else:
+            print("No temporary images files to clear.")
+
+
+def config_tmp():
+    # create shark_tmp if it does not exist
+    if not os.path.exists(shark_tmp):
+        os.mkdir(shark_tmp)
+
+    clear_tmp_mlir()
+    clear_tmp_imgs()

apps/stable_diffusion/profiling_with_iree.md

@@ -1,87 +0,0 @@
-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

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

apps/stable_diffusion/scripts/telegram_bot.py

@@ -1,240 +0,0 @@
-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

@@ -1,331 +0,0 @@
-import os
-
-if "AMD_ENABLE_LLPC" not in os.environ:
-    os.environ["AMD_ENABLE_LLPC"] = "1"
-
-import sys
-import json
-import torch
-import re
-import time
-from pathlib import Path
-from PIL import PngImagePlugin
-from datetime import datetime as dt
-from dataclasses import dataclass
-from csv import DictWriter
-from apps.stable_diffusion.src import (
-    args,
-    Text2ImagePipeline,
-    get_schedulers,
-    set_init_device_flags,
-    utils,
-)
-
-
-@dataclass
-class Config:
-    model_id: str
-    ckpt_loc: str
-    precision: str
-    batch_size: int
-    max_length: int
-    height: int
-    width: int
-    device: str
-
-
-# This has to come before importing cache objects
-if args.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")
-    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)
-
-
-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.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.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={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 += txt2img_obj.log
-    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
-
-    return generated_imgs, text_output
-
-
-if __name__ == "__main__":
-    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.precision,
-        args.max_length,
-        args.batch_size,
-        args.height,
-        args.width,
-        args.use_base_vae,
-        args.use_tuned,
-    )
-
-    for run in range(args.runs):
-        if run > 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 --runs=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

@@ -1,79 +0,0 @@
-# -*- 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/css/*', 'css' ),
-         ( 'web/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

@@ -1,77 +0,0 @@
-# -*- 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

@@ -1,8 +0,0 @@
-from apps.stable_diffusion.src.utils import (
-    args,
-    set_init_device_flags,
-    prompt_examples,
-    get_available_devices,
-)
-from apps.stable_diffusion.src.pipelines import Text2ImagePipeline
-from apps.stable_diffusion.src.schedulers import get_schedulers

apps/stable_diffusion/src/models/__init__.py

@@ -1,11 +0,0 @@
-from apps.stable_diffusion.src.models.model_wrappers import (
-    SharkifyStableDiffusionModel,
-)
-from apps.stable_diffusion.src.models.opt_params import (
-    get_vae,
-    get_unet,
-    get_clip,
-    get_tokenizer,
-    get_params,
-    get_variant_version,
-)

apps/stable_diffusion/src/models/model_wrappers.py

@@ -1,295 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel
-from transformers import CLIPTextModel
-from collections import defaultdict
-import torch
-import traceback
-import re
-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,
-)
-
-
-# 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):
-            if "batch_size" in shape[i]:
-                mul_val = int(shape[i].split("*")[0])
-                new_shape.append(batch_size * mul_val)
-        else:
-            new_shape.append(shape[i])
-    return new_shape
-
-
-# Get the input info for various models i.e. "unet", "clip", "vae".
-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,
-        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
-        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"
-        # We need a better naming convention for the .vmfbs because despite
-        # using the custom model variant the .vmfb names remain the same and
-        # it'll always pick up the compiled .vmfb instead of compiling the
-        # custom model.
-        # So, currently, we add `self.model_id` in the `self.model_name` of
-        # .vmfb file.
-        # TODO: Have a better way of naming the vmfbs using self.model_name.
-        model_name = re.sub(r"\W+", "_", self.model_id)
-        if model_name[0] == "_":
-            model_name = model_name[1:]
-        self.model_name = self.model_name + "_" + 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(self):
-        class VaeModel(torch.nn.Module):
-            def __init__(self, model_id=self.model_id, base_vae=self.base_vae):
-                super().__init__()
-                self.vae = AutoencoderKL.from_pretrained(
-                    model_id,
-                    subfolder="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
-        vae_name = "base_vae" if self.base_vae else "vae"
-        shark_vae = compile_through_fx(
-            vae,
-            inputs,
-            is_f16=is_f16,
-            use_tuned=self.use_tuned,
-            model_name=vae_name + self.model_name,
-            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="unet" + self.model_name,
-            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="clip" + self.model_name,
-            extra_args=get_opt_flags("clip", precision="fp32"),
-        )
-        return shark_clip
-
-    # Compiles Clip, Unet and Vae with `base_model_id` as defining their input
-    # configiration.
-    def compile_all(self, base_model_id):
-        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()
-        compiled_vae = self.get_vae()
-        compiled_clip = self.get_clip()
-        
-        return compiled_clip, compiled_unet, compiled_vae
-
-    def __call__(self):
-        # Step 1:
-        # --  Fetch all vmfbs for the model, if present, else delete the lot.
-        vmfbs = fetch_or_delete_vmfbs(
-            self.model_name, self.base_vae, 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
-        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)
-
-        # 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:
-                compiled_clip, compiled_unet, compiled_vae = self.compile_all(model_id)
-            except Exception as e:
-                if args.enable_stack_trace:
-                    traceback.print_exc()
-                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
-            return compiled_clip, compiled_unet, compiled_vae
-        sys.exit(
-            "Cannot compile the model. Please re-run the command with `--enable_stack_trace` flag and create an issue with detailed log at https://github.com/nod-ai/SHARK/issues"
-        )

apps/stable_diffusion/src/models/opt_params.py

@@ -1,89 +0,0 @@
-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", "v2_1base"],
-    "dreamlike-art/dreamlike-diffusion-1.0": ["dreamlike", "v2_1base"],
-    "prompthero/openjourney": ["openjourney", "v2_1base"],
-    "wavymulder/Analog-Diffusion": ["analogdiffusion", "v2_1base"],
-    "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"],
-}
-
-
-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():
-    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

@@ -1,3 +0,0 @@
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_txt2img import (
-    Text2ImagePipeline,
-)

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_txt2img.py

@@ -1,135 +0,0 @@
-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

@@ -1,206 +0,0 @@
-import torch
-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,
-    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,
-        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 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,
-        precision: str,
-        max_length: int,
-        batch_size: int,
-        height: int,
-        width: int,
-        use_base_vae: bool,
-        use_tuned: bool,
-    ):
-        if import_mlir:
-            # TODO: Delet this when on-the-fly tuning of models work.
-            use_tuned = False
-            mlir_import = SharkifyStableDiffusionModel(
-                model_id,
-                ckpt_loc,
-                precision,
-                max_len=max_length,
-                batch_size=batch_size,
-                height=height,
-                width=width,
-                use_base_vae=use_base_vae,
-                use_tuned=use_tuned,
-            )
-            clip, unet, vae = mlir_import()
-            return cls(vae, clip, get_tokenizer(), unet, scheduler)
-        return cls(
-            get_vae(), get_clip(), get_tokenizer(), get_unet(), scheduler
-        )

apps/stable_diffusion/src/schedulers/__init__.py

@@ -1,4 +0,0 @@
-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

@@ -1,51 +0,0 @@
-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

@@ -1,143 +0,0 @@
-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")
-
-        if args.import_mlir:
-            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,
-            )
-        else:
-            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
-            )
-
-    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

@@ -1,27 +0,0 @@
-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,
-)

apps/stable_diffusion/src/utils/profiler.py

@@ -1,18 +0,0 @@
-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

@@ -1,37 +0,0 @@
-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

@@ -1,98 +0,0 @@
-{
-    "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": {
-            "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": {
-            "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

@@ -1,21 +0,0 @@
-[
-  {
-    "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",
-    "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

@@ -1,82 +0,0 @@
-[
-  {
-    "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/v1_4/vae/fp32/length_77/untuned":"vae_1dec_fp32",
-    "stablediffusion/v1_4/clip/fp32/length_77/untuned":"clip_18dec_fp32",
-    "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",
-    "anythingv3/v2_1base/unet/fp16/length_77/untuned":"av3_unet_19dec_fp16",
-    "anythingv3/v2_1base/unet/fp16/length_77/tuned":"av3_unet_19dec_fp16_tuned",
-    "anythingv3/v2_1base/unet/fp16/length_77/tuned/cuda":"av3_unet_19dec_fp16_cuda_tuned",
-    "anythingv3/v2_1base/unet/fp32/length_77/untuned":"av3_unet_19dec_fp32",
-    "anythingv3/v2_1base/vae/fp16/length_77/untuned":"av3_vae_19dec_fp16",
-    "anythingv3/v2_1base/vae/fp16/length_77/tuned":"av3_vae_19dec_fp16_tuned",
-    "anythingv3/v2_1base/vae/fp16/length_77/tuned/cuda":"av3_vae_19dec_fp16_cuda_tuned",
-    "anythingv3/v2_1base/vae/fp16/length_77/untuned/base":"av3_vaebase_22dec_fp16",
-    "anythingv3/v2_1base/vae/fp32/length_77/untuned":"av3_vae_19dec_fp32",
-    "anythingv3/v2_1base/vae/fp32/length_77/untuned/base":"av3_vaebase_22dec_fp32",
-    "anythingv3/v2_1base/clip/fp32/length_77/untuned":"av3_clip_19dec_fp32",
-    "analogdiffusion/v2_1base/unet/fp16/length_77/untuned":"ad_unet_19dec_fp16",
-    "analogdiffusion/v2_1base/unet/fp16/length_77/tuned":"ad_unet_19dec_fp16_tuned",
-    "analogdiffusion/v2_1base/unet/fp16/length_77/tuned/cuda":"ad_unet_19dec_fp16_cuda_tuned",
-    "analogdiffusion/v2_1base/unet/fp32/length_77/untuned":"ad_unet_19dec_fp32",
-    "analogdiffusion/v2_1base/vae/fp16/length_77/untuned":"ad_vae_19dec_fp16",
-    "analogdiffusion/v2_1base/vae/fp16/length_77/tuned":"ad_vae_19dec_fp16_tuned",
-    "analogdiffusion/v2_1base/vae/fp16/length_77/tuned/cuda":"ad_vae_19dec_fp16_cuda_tuned",
-    "analogdiffusion/v2_1base/vae/fp16/length_77/untuned/base":"ad_vaebase_22dec_fp16",
-    "analogdiffusion/v2_1base/vae/fp32/length_77/untuned":"ad_vae_19dec_fp32",
-    "analogdiffusion/v2_1base/vae/fp32/length_77/untuned/base":"ad_vaebase_22dec_fp32",
-    "analogdiffusion/v2_1base/clip/fp32/length_77/untuned":"ad_clip_19dec_fp32",
-    "openjourney/v2_1base/unet/fp16/length_64/untuned":"oj_unet_22dec_fp16_64",
-    "openjourney/v2_1base/unet/fp32/length_64/untuned":"oj_unet_22dec_fp32_64",
-    "openjourney/v2_1base/vae/fp16/length_77/untuned":"oj_vae_22dec_fp16",
-    "openjourney/v2_1base/vae/fp16/length_77/untuned/base":"oj_vaebase_22dec_fp16",
-    "openjourney/v2_1base/vae/fp32/length_77/untuned":"oj_vae_22dec_fp32",
-    "openjourney/v2_1base/vae/fp32/length_77/untuned/base":"oj_vaebase_22dec_fp32",
-    "openjourney/v2_1base/clip/fp32/length_64/untuned":"oj_clip_22dec_fp32_64",
-    "dreamlike/v2_1base/unet/fp16/length_77/untuned":"dl_unet_23dec_fp16_77",
-    "dreamlike/v2_1base/unet/fp32/length_77/untuned":"dl_unet_23dec_fp32_77",
-    "dreamlike/v2_1base/vae/fp16/length_77/untuned":"dl_vae_23dec_fp16",
-    "dreamlike/v2_1base/vae/fp16/length_77/untuned/base":"dl_vaebase_23dec_fp16",
-    "dreamlike/v2_1base/vae/fp32/length_77/untuned":"dl_vae_23dec_fp32",
-    "dreamlike/v2_1base/vae/fp32/length_77/untuned/base":"dl_vaebase_23dec_fp32",
-    "dreamlike/v2_1base/clip/fp32/length_77/untuned":"dl_clip_23dec_fp32_77"
-  }
-]

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

@@ -1,84 +0,0 @@
-{
-  "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-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}))"
-        ]
-      }
-    }
-  },
-  "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

@@ -1,8 +0,0 @@
-[["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

@@ -1,234 +0,0 @@
-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
-
-
-# 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/"
-    config_version = version
-    if variant in ["anythingv3", "analogdiffusion"]:
-        args.max_length = 77
-        config_version = "v1_4"
-    if args.annotation_model == "vae":
-        args.max_length = 77
-    device = get_device()
-    config_name = f"{args.annotation_model}_{config_version}_{args.precision}_len{args.max_length}_{device}.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):
-    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 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()
-
-    with open(out_file_path, "w") as f:
-        f.write(str(winograd_model))
-        f.close()
-    return bytecode, out_file_path
-
-
-def dump_after_mlir(input_mlir, model_name, use_winograd):
-    if use_winograd:
-        dump_after = "iree-linalg-ext-convert-conv2d-to-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:
-        dump_after = "iree-preprocessing-pad-linalg-ops"
-        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}))' "
-        )
-
-    device_spec_args = ""
-    device = get_device()
-    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 += flag + " "
-    elif device == "vulkan":
-        device_spec_args = (
-            f"--iree-vulkan-target-triple={args.iree_vulkan_target_triple} "
-        )
-    print("Applying tuned configs on", model_name)
-
-    run_cmd(
-        f"iree-compile {input_mlir} "
-        "--iree-input-type=tm_tensor "
-        f"--iree-hal-target-backends={iree_target_map(device)} "
-        f"{device_spec_args}"
-        f"{preprocess_flag}"
-        "--iree-stream-resource-index-bits=64 "
-        "--iree-vm-target-index-bits=64 "
-        f"--mlir-print-ir-after={dump_after} "
-        "--compile-to=flow "
-        f"2>{args.annotation_output}/dump_after_winograd.mlir "
-    )
-
-
-# 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_after_mlir(input_mlir, model_name, use_winograd)
-
-    # Annotate the model with lowering configs in the config file
-    with create_context() as ctx:
-        tuned_model = model_annotation(
-            ctx,
-            input_contents=f"{args.annotation_output}/dump_after_winograd.mlir",
-            config_path=lowering_config_dir,
-            search_op="all",
-        )
-
-    # Remove the intermediate mlir and save the final annotated model
-    os.remove(f"{args.annotation_output}/dump_after_winograd.mlir")
-    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"
-
-    bytecode_stream = io.BytesIO()
-    tuned_model.operation.write_bytecode(bytecode_stream)
-    bytecode = bytecode_stream.getvalue()
-
-    with open(out_file_path, "w") as f:
-        f.write(str(tuned_model))
-        f.close()
-    return bytecode, out_file_path
-
-
-def sd_model_annotation(mlir_model, model_name, model_from_tank=False):
-    device = get_device()
-    if args.annotation_model == "unet" and device == "vulkan":
-        use_winograd = True
-        winograd_config_dir = load_winograd_configs()
-        winograd_model, model_path = annotate_with_winograd(
-            mlir_model, winograd_config_dir, model_name
-        )
-        lowering_config_dir = load_lower_configs()
-        tuned_model, output_path = annotate_with_lower_configs(
-            model_path, 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, output_path = annotate_with_winograd(
-            mlir_model, winograd_config_dir, model_name
-        )
-    else:
-        use_winograd = False
-        if model_from_tank:
-            mlir_model = f"{WORKDIR}{model_name}_torch/{model_name}_torch.mlir"
-        else:
-            # Just use this function to convert bytecode to string
-            orig_model, model_path = annotate_with_winograd(
-                mlir_model, "", model_name
-            )
-            mlir_model = model_path
-        lowering_config_dir = load_lower_configs()
-        tuned_model, output_path = annotate_with_lower_configs(
-            mlir_model, lowering_config_dir, model_name, use_winograd
-        )
-    print(f"Saved the annotated mlir in {output_path}.")
-    return tuned_model
-
-
-if __name__ == "__main__":
-    mlir_model, model_name = load_model_from_tank()
-    sd_model_annotation(mlir_model, model_name, model_from_tank=True)

apps/stable_diffusion/src/utils/stable_args.py

@@ -1,345 +0,0 @@
-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",
-    action="append",
-    default=[],
-    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(
-    "--batch_size",
-    type=int,
-    default=1,
-    choices=range(1, 4),
-    help="the number of inferences to be made in a single `run`.",
-)
-
-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.",
-)
-
-##############################################################################
-### 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(
-    "--runs",
-    type=int,
-    default=1,
-    help="number of images 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(
-    "--hf_model_id",
-    type=str,
-    default="stabilityai/stable-diffusion-2-1-base",
-    help="The repo-id of hugging face.",
-)
-
-p.add_argument(
-    "--enable_stack_trace",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="Enable showing the stack trace when retrying the base model configuration",
-)
-
-##############################################################################
-### 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=False,
-    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 pregress 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(
-    "--use_winograd",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="Apply Winograd on selected conv ops.",
-)
-
-args, unknown = p.parse_known_args()

apps/stable_diffusion/src/utils/utils.py

@@ -1,460 +0,0 @@
-import os
-import gc
-import json
-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, functools, operator
-from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
-    load_pipeline_from_original_stable_diffusion_ckpt,
-)
-
-
-def get_vmfb_path_name(model_name):
-    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")
-    return [vmfb_path, extended_name]
-
-
-def _compile_module(shark_module, model_name, extra_args=[]):
-    if args.load_vmfb or args.save_vmfb:
-        [vmfb_path, extended_name] = 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(), 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.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 == "prompthero/openjourney"
-        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 "rdna3" not in args.iree_vulkan_target_triple
-    ):
-        args.use_tuned = False
-
-    elif "cuda" in args.device and get_cuda_sm_cc() not in ["sm_80"]:
-        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",
-    ]:
-        args.import_mlir = True
-
-    elif args.height != 512 or args.width != 512 or args.batch_size != 1:
-        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_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 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 and Vae, in case all three of them
-# are present; deletes them otherwise.
-def fetch_or_delete_vmfbs(basic_model_name, use_base_vae, precision="fp32"):
-    model_name = ["clip", "unet", "base_vae" if use_base_vae else "vae"]
-    vmfb_path = [
-        get_vmfb_path_name(model + basic_model_name)[0] for model in model_name
-    ]
-    vmfb_present = [os.path.isfile(vmfb) for vmfb in vmfb_path]
-    all_vmfb_present = functools.reduce(operator.__and__, vmfb_present)
-    compiled_models = [None] * 3
-    # We need to delete vmfbs only if some of the models were compiled.
-    if not all_vmfb_present:
-        for i in range(len(vmfb_path)):
-            if vmfb_present[i]:
-                os.remove(vmfb_path[i])
-                print("Deleted: ", vmfb_path[i])
-    else:
-        for i in range(len(vmfb_path)):
-            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

apps/stable_diffusion/stable_diffusion_amd.md

@@ -1,70 +0,0 @@
-# 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 specific AMD Drivers (AMD latest may not have all the fixes).
-
-### AMD KB Drivers for RDNA2 and RDNA3:
-
-*AMD Software: Adrenalin Edition 22.11.1 for MLIR/IREE Driver Version 22.20.29.09 for Windows® 10 and Windows® 11 (Windows Driver Store Version 31.0.12029.9003)*
-
-First, for RDNA2 users, download this special driver in a folder of your choice. We recommend you keep the installation files around, since you may need to re-install it later, if Windows Update decides to overwrite it:
-https://www.amd.com/en/support/kb/release-notes/rn-rad-win-22-11-1-mlir-iree
-
-For RDNA3, the latest driver 23.1.2 supports MLIR/IREE as well: https://www.amd.com/en/support/kb/release-notes/rn-rad-win-23-1-2-kb
-
-KNOWN ISSUES with this special AMD driver:
-* `Windows Update` may (depending how it's configured) automatically install a new official AMD driver that overwrites this IREE-specific driver. If Stable Diffusion used to work, then a few days later, it slows down a lot or produces incorrect results (e.g. black images), this may be the cause. To fix this problem, please check the installed driver version, and re-install the special driver if needed. (TODO: document how to prevent this `Windows Update` behavior!)
-* Some people using this special driver experience mouse pointer accuracy issues, especially if using a larger-than-default mouse pointer. The clicked point isn't centered properly. One possible work-around is to reset the pointer size to "1" in "Change pointer size and color".
-
-## Installation
-
-Download the latest Windows SHARK SD binary [492 here](https://github.com/nod-ai/SHARK/releases/download/20230203.492/shark_sd_20230203_492.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 
-  * clear the Vulkan shader cache: For Windows users this can be done by clearing the contents of `C:\Users\%username%\AppData\Local\AMD\VkCache\`. On Linux the same cache is typically located at `~/.cache/AMD/VkCache/`.
-  * clear the `huggingface` cache. In Windows, this is `C:\Users\%username%\.cache\huggingface`.
-
-## 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 about 10-15 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/?__theme=dark.
-
-## 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

@@ -1,15 +0,0 @@
-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/css/sd_dark_theme.css

@@ -1,209 +0,0 @@
-
-/* 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/index.py

@@ -1,264 +0,0 @@
-import os
-import sys
-from pathlib import Path
-import glob
-
-if "AMD_ENABLE_LLPC" not in os.environ:
-    os.environ["AMD_ENABLE_LLPC"] = "1"
-
-if sys.platform == "darwin":
-    os.environ["DYLD_LIBRARY_PATH"] = "/usr/local/lib"
-
-
-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)
-
-
-import gradio as gr
-from PIL import Image
-from apps.stable_diffusion.src import (
-    prompt_examples,
-    args,
-    get_available_devices,
-)
-from apps.stable_diffusion.scripts import txt2img_inf
-
-nodlogo_loc = resource_path("logos/nod-logo.png")
-sdlogo_loc = resource_path("logos/sd-demo-logo.png")
-
-
-demo_css = resource_path("css/sd_dark_theme.css")
-
-
-with gr.Blocks(title="Stable Diffusion", css=demo_css) as shark_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="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",
-                    )
-
-                with gr.Group(elem_id="prompt_box_outer"):
-                    prompt = gr.Textbox(
-                        label="Prompt",
-                        value="cyberpunk forest by Salvador Dali",
-                        lines=1,
-                        elem_id="prompt_box",
-                    )
-                    negative_prompt = gr.Textbox(
-                        label="Negative Prompt",
-                        value="trees, green",
-                        lines=1,
-                        elem_id="prompt_box",
-                    )
-                with gr.Accordion(label="Advanced Options", open=False):
-                    with gr.Row():
-                        scheduler = gr.Dropdown(
-                            label="Scheduler",
-                            value="SharkEulerDiscrete",
-                            choices=[
-                                "DDIM",
-                                "PNDM",
-                                "LMSDiscrete",
-                                "DPMSolverMultistep",
-                                "EulerDiscrete",
-                                "EulerAncestralDiscrete",
-                                "SharkEulerDiscrete",
-                            ],
-                        )
-                        with gr.Group():
-                            save_metadata_to_png = gr.Checkbox(
-                                label="Save prompt information to PNG",
-                                value=True,
-                                interactive=True,
-                            )
-                            save_metadata_to_json = gr.Checkbox(
-                                label="Save prompt information to JSON file",
-                                value=False,
-                                interactive=True,
-                            )
-                    with gr.Row():
-                        height = gr.Slider(
-                            384, 786, value=512, step=8, label="Height"
-                        )
-                        width = gr.Slider(
-                            384, 786, value=512, step=8, label="Width"
-                        )
-                        precision = gr.Radio(
-                            label="Precision",
-                            value="fp16",
-                            choices=[
-                                "fp16",
-                                "fp32",
-                            ],
-                            visible=False,
-                        )
-                        max_length = gr.Radio(
-                            label="Max Length",
-                            value=64,
-                            choices=[
-                                64,
-                                77,
-                            ],
-                            visible=False,
-                        )
-                    with gr.Row():
-                        steps = gr.Slider(
-                            1, 100, value=50, step=1, label="Steps"
-                        )
-                        guidance_scale = gr.Slider(
-                            0,
-                            50,
-                            value=7.5,
-                            step=0.1,
-                            label="CFG Scale",
-                        )
-                    with gr.Row():
-                        batch_count = gr.Slider(
-                            1,
-                            10,
-                            value=1,
-                            step=1,
-                            label="Batch Count",
-                            interactive=True,
-                        )
-                        batch_size = gr.Slider(
-                            1,
-                            4,
-                            value=1,
-                            step=1,
-                            label="Batch Size",
-                            interactive=True,
-                        )
-                with gr.Row():
-                    seed = gr.Number(value=-1, precision=0, label="Seed")
-                    available_devices = get_available_devices()
-                    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)
-
-shark_web.queue()
-shark_web.launch(
-    share=args.share,
-    inbrowser=True,
-    server_name="0.0.0.0",
-    server_port=args.server_port,
-)

benchmarks/hf_model_benchmark.py

@@ -1,22 +0,0 @@
-import torch
-from shark.parser import parser
-from benchmarks.hf_transformer import SharkHFBenchmarkRunner
-
-parser.add_argument(
-    "--model_name",
-    type=str,
-    required=True,
-    help='Specifies name of HF model to benchmark. (For exmaple "microsoft/MiniLM-L12-H384-uncased"',
-)
-load_args, unknown = parser.parse_known_args()
-
-if __name__ == "__main__":
-    model_name = load_args.model_name
-    test_input = torch.randint(2, (1, 128))
-    shark_module = SharkHFBenchmarkRunner(
-        model_name, (test_input,), jit_trace=True
-    )
-    shark_module.benchmark_c()
-    shark_module.benchmark_python((test_input,))
-    shark_module.benchmark_torch(test_input)
-    shark_module.benchmark_onnx(test_input)

benchmarks/hf_transformer.py

@@ -1,181 +0,0 @@
-import torch
-from shark.shark_benchmark_runner import SharkBenchmarkRunner
-from shark.parser import shark_args
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from onnxruntime.transformers.benchmark import (
-    run_pytorch,
-    run_tensorflow,
-    run_onnxruntime,
-)
-from onnxruntime.transformers.huggingface_models import MODELS
-from onnxruntime.transformers.benchmark_helper import ConfigModifier, Precision
-import os
-import psutil
-
-
-class OnnxFusionOptions(object):
-    def __init__(self):
-        self.disable_gelu = False
-        self.disable_layer_norm = False
-        self.disable_attention = False
-        self.disable_skip_layer_norm = False
-        self.disable_embed_layer_norm = False
-        self.disable_bias_skip_layer_norm = False
-        self.disable_bias_gelu = False
-        self.enable_gelu_approximation = False
-        self.use_mask_index = False
-        self.no_attention_mask = False
-
-
-class HuggingFaceLanguage(torch.nn.Module):
-    def __init__(self, hf_model_name):
-        super().__init__()
-        self.model = AutoModelForSequenceClassification.from_pretrained(
-            hf_model_name,  # The pretrained model.
-            num_labels=2,  # The number of output labels--2 for binary classification.
-            output_attentions=False,  # Whether the model returns attentions weights.
-            output_hidden_states=False,  # Whether the model returns all hidden-states.
-            torchscript=True,
-        )
-
-    def forward(self, tokens):
-        return self.model.forward(tokens)[0]
-
-
-class SharkHFBenchmarkRunner(SharkBenchmarkRunner):
-    # SharkRunner derived class with Benchmarking capabilities.
-    def __init__(
-        self,
-        model_name: str,
-        input: tuple,
-        dynamic: bool = False,
-        device: str = None,
-        jit_trace: bool = False,
-        from_aot: bool = False,
-        frontend: str = "torch",
-    ):
-        self.device = device if device is not None else shark_args.device
-        if self.device == "gpu":
-            raise ValueError(
-                "Currently GPU Benchmarking is not supported due to OOM from ORT."
-            )
-        self.model_name = model_name
-        model = HuggingFaceLanguage(model_name)
-        SharkBenchmarkRunner.__init__(
-            self,
-            model,
-            input,
-            dynamic,
-            self.device,
-            jit_trace,
-            from_aot,
-            frontend,
-        )
-
-    def benchmark_torch(self, inputs):
-        use_gpu = self.device == "gpu"
-        # Set set the model's layer number to automatic.
-        config_modifier = ConfigModifier(None)
-        num_threads = psutil.cpu_count(logical=False)
-        batch_sizes = [inputs.shape[0]]
-        sequence_lengths = [inputs.shape[-1]]
-        cache_dir = os.path.join(".", "cache_models")
-        verbose = False
-        result = run_pytorch(
-            use_gpu,
-            [self.model_name],
-            None,
-            config_modifier,
-            Precision.FLOAT32,
-            num_threads,
-            batch_sizes,
-            sequence_lengths,
-            shark_args.num_iterations,
-            False,
-            cache_dir,
-            verbose,
-        )
-        print(
-            f"ONNX Pytorch-benchmark:{result[0]['QPS']} iter/second, Total Iterations:{shark_args.num_iterations}"
-        )
-
-    # TODO: Currently non-functional due to TF runtime error. There might be some issue with, initializing TF.
-    def benchmark_tf(self, inputs):
-        use_gpu = self.device == "gpu"
-        # Set set the model's layer number to automatic.
-        config_modifier = ConfigModifier(None)
-        num_threads = psutil.cpu_count(logical=False)
-        batch_sizes = [inputs.shape[0]]
-        sequence_lengths = [inputs.shape[-1]]
-        cache_dir = os.path.join(".", "cache_models")
-        verbose = False
-        result = run_tensorflow(
-            use_gpu,
-            [self.model_name],
-            None,
-            config_modifier,
-            Precision.FLOAT32,
-            num_threads,
-            batch_sizes,
-            sequence_lengths,
-            shark_args.num_iterations,
-            cache_dir,
-            verbose,
-        )
-        print(
-            f"ONNX TF-benchmark:{result[0]['QPS']} iter/second, Total Iterations:{shark_args.num_iterations}"
-        )
-
-    def benchmark_onnx(self, inputs):
-        if self.model_name not in MODELS:
-            print(
-                f"{self.model_name} is currently not supported in ORT's HF. Check \
-https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/huggingface_models.py \
-for currently supported models. Exiting benchmark ONNX."
-            )
-            return
-        use_gpu = self.device == "gpu"
-        num_threads = psutil.cpu_count(logical=False)
-        batch_sizes = [inputs.shape[0]]
-        sequence_lengths = [inputs.shape[-1]]
-        cache_dir = os.path.join(".", "cache_models")
-        onnx_dir = os.path.join(".", "onnx_models")
-        verbose = False
-        input_counts = [1]
-        optimize_onnx = True
-        validate_onnx = False
-        disable_ort_io_binding = False
-        use_raw_attention_mask = True
-        model_fusion_statistics = {}
-        overwrite = False
-        model_source = "pt"  # Either "pt" or "tf"
-        provider = None
-        config_modifier = ConfigModifier(None)
-        onnx_args = OnnxFusionOptions()
-        result = run_onnxruntime(
-            use_gpu,
-            provider,
-            [self.model_name],
-            None,
-            config_modifier,
-            Precision.FLOAT32,
-            num_threads,
-            batch_sizes,
-            sequence_lengths,
-            shark_args.num_iterations,
-            input_counts,
-            optimize_onnx,
-            validate_onnx,
-            cache_dir,
-            onnx_dir,
-            verbose,
-            overwrite,
-            disable_ort_io_binding,
-            use_raw_attention_mask,
-            model_fusion_statistics,
-            model_source,
-            onnx_args,
-        )
-        print(
-            f"ONNX ORT-benchmark:{result[0]['QPS']} iter/second, Total Iterations:{shark_args.num_iterations}"
-        )

benchmarks/tests/test_benchmark.py

@@ -1,231 +0,0 @@
-from shark.shark_inference import SharkInference
-from shark.iree_utils._common import check_device_drivers
-
-import torch
-import tensorflow as tf
-import numpy as np
-import torchvision.models as models
-from transformers import (
-    AutoModelForSequenceClassification,
-    BertTokenizer,
-    TFBertModel,
-)
-import importlib
-import pytest
-import unittest
-
-torch.manual_seed(0)
-gpus = tf.config.experimental.list_physical_devices("GPU")
-for gpu in gpus:
-    tf.config.experimental.set_memory_growth(gpu, True)
-
-##################### Tensorflow Hugging Face LM Models ###################################
-MAX_SEQUENCE_LENGTH = 512
-BATCH_SIZE = 1
-
-# Create a set of 2-dimensional inputs
-tf_bert_input = [
-    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
-    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
-    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
-]
-
-
-class TFHuggingFaceLanguage(tf.Module):
-    def __init__(self, hf_model_name):
-        super(TFHuggingFaceLanguage, self).__init__()
-        # Create a BERT trainer with the created network.
-        self.m = TFBertModel.from_pretrained(hf_model_name, from_pt=True)
-
-        # Invoke the trainer model on the inputs. This causes the layer to be built.
-        self.m.predict = lambda x, y, z: self.m.call(
-            input_ids=x, attention_mask=y, token_type_ids=z, training=False
-        )
-
-    @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)
-
-
-def get_TFhf_model(name):
-    model = TFHuggingFaceLanguage(name)
-    tokenizer = BertTokenizer.from_pretrained(name)
-    text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(
-        text,
-        padding="max_length",
-        truncation=True,
-        max_length=MAX_SEQUENCE_LENGTH,
-    )
-    for key in encoded_input:
-        encoded_input[key] = tf.expand_dims(
-            tf.convert_to_tensor(encoded_input[key]), 0
-        )
-    test_input = (
-        encoded_input["input_ids"],
-        encoded_input["attention_mask"],
-        encoded_input["token_type_ids"],
-    )
-    actual_out = model.forward(*test_input)
-    return model, test_input, actual_out
-
-
-##################### Hugging Face LM Models ###################################
-
-
-class HuggingFaceLanguage(torch.nn.Module):
-    def __init__(self, hf_model_name):
-        super().__init__()
-        self.model = AutoModelForSequenceClassification.from_pretrained(
-            hf_model_name,  # The pretrained model.
-            num_labels=2,  # The number of output labels--2 for binary classification.
-            output_attentions=False,  # Whether the model returns attentions weights.
-            output_hidden_states=False,  # Whether the model returns all hidden-states.
-            torchscript=True,
-        )
-
-    def forward(self, tokens):
-        return self.model.forward(tokens)[0]
-
-
-def get_hf_model(name):
-    model = HuggingFaceLanguage(name)
-    # TODO: Currently the test input is set to (1,128)
-    test_input = torch.randint(2, (1, 128))
-    actual_out = model(test_input)
-    return model, test_input, actual_out
-
-
-################################################################################
-
-##################### Torch Vision Models    ###################################
-
-
-class VisionModule(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-        self.train(False)
-
-    def forward(self, input):
-        return self.model.forward(input)
-
-
-def get_vision_model(torch_model):
-    model = VisionModule(torch_model)
-    # TODO: Currently the test input is set to (1,128)
-    test_input = torch.randn(1, 3, 224, 224)
-    actual_out = model(test_input)
-    return model, test_input, actual_out
-
-
-#############################   Benchmark Tests ####################################
-
-pytest_benchmark_param = pytest.mark.parametrize(
-    ("dynamic", "device"),
-    [
-        pytest.param(False, "cpu"),
-        # TODO: Language models are failing for dynamic case..
-        pytest.param(True, "cpu", marks=pytest.mark.skip),
-        pytest.param(
-            False,
-            "gpu",
-            marks=pytest.mark.skipif(
-                check_device_drivers("gpu"), reason="nvidia-smi not found"
-            ),
-        ),
-        pytest.param(True, "gpu", marks=pytest.mark.skip),
-        pytest.param(
-            False,
-            "vulkan",
-            marks=pytest.mark.skipif(
-                check_device_drivers("vulkan"),
-                reason="vulkaninfo not found, install from https://github.com/KhronosGroup/MoltenVK/releases",
-            ),
-        ),
-        pytest.param(
-            True,
-            "vulkan",
-            marks=pytest.mark.skipif(
-                check_device_drivers("vulkan"),
-                reason="vulkaninfo not found, install from https://github.com/KhronosGroup/MoltenVK/releases",
-            ),
-        ),
-    ],
-)
-
-
-@pytest.mark.skipif(
-    importlib.util.find_spec("iree.tools") is None,
-    reason="Cannot find tools to import TF",
-)
-@pytest_benchmark_param
-def test_bench_minilm_torch(dynamic, device):
-    model, test_input, act_out = get_hf_model(
-        "microsoft/MiniLM-L12-H384-uncased"
-    )
-    shark_module = SharkInference(
-        model,
-        (test_input,),
-        device=device,
-        dynamic=dynamic,
-        jit_trace=True,
-        benchmark_mode=True,
-    )
-    try:
-        # If becnhmarking succesful, assert success/True.
-        shark_module.compile()
-        shark_module.benchmark_all((test_input,))
-        assert True
-    except Exception as e:
-        # If anything happen during benchmarking, assert False/failure.
-        assert False
-
-
-@pytest.mark.skipif(
-    importlib.util.find_spec("iree.tools") is None,
-    reason="Cannot find tools to import TF",
-)
-@pytest_benchmark_param
-def test_bench_distilbert(dynamic, device):
-    model, test_input, act_out = get_TFhf_model("distilbert-base-uncased")
-    shark_module = SharkInference(
-        model,
-        test_input,
-        device=device,
-        dynamic=dynamic,
-        jit_trace=True,
-        benchmark_mode=True,
-    )
-    try:
-        # If becnhmarking succesful, assert success/True.
-        shark_module.set_frontend("tensorflow")
-        shark_module.compile()
-        shark_module.benchmark_all(test_input)
-        assert True
-    except Exception as e:
-        # If anything happen during benchmarking, assert False/failure.
-        assert False
-
-
-@pytest.mark.skip(reason="XLM Roberta too large to test.")
-@pytest_benchmark_param
-def test_bench_xlm_roberta(dynamic, device):
-    model, test_input, act_out = get_TFhf_model("xlm-roberta-base")
-    shark_module = SharkInference(
-        model,
-        test_input,
-        device=device,
-        dynamic=dynamic,
-        jit_trace=True,
-        benchmark_mode=True,
-    )
-    try:
-        # If becnhmarking succesful, assert success/True.
-        shark_module.set_frontend("tensorflow")
-        shark_module.compile()
-        shark_module.benchmark_all(test_input)
-        assert True
-    except Exception as e:
-        # If anything happen during benchmarking, assert False/failure.
-        assert False

benchmarks/tests/test_hf_benchmark.py

@@ -1,45 +0,0 @@
-import torch
-from benchmarks.hf_transformer import SharkHFBenchmarkRunner
-import importlib
-import pytest
-
-torch.manual_seed(0)
-
-############################# HF Benchmark Tests ####################################
-
-# Test running benchmark module without failing.
-pytest_benchmark_param = pytest.mark.parametrize(
-    ("dynamic", "device"),
-    [
-        pytest.param(False, "cpu"),
-        # TODO: Language models are failing for dynamic case..
-        pytest.param(True, "cpu", marks=pytest.mark.skip),
-    ],
-)
-
-
-@pytest.mark.skipif(
-    importlib.util.find_spec("onnxruntime") is None,
-    reason="Cannot find ONNXRUNTIME.",
-)
-@pytest_benchmark_param
-def test_HFbench_minilm_torch(dynamic, device):
-    model_name = "bert-base-uncased"
-    test_input = torch.randint(2, (1, 128))
-    try:
-        shark_module = SharkHFBenchmarkRunner(
-            model_name,
-            (test_input,),
-            jit_trace=True,
-            dynamic=dynamic,
-            device=device,
-        )
-        shark_module.benchmark_c()
-        shark_module.benchmark_python((test_input,))
-        shark_module.benchmark_torch(test_input)
-        shark_module.benchmark_onnx(test_input)
-        # If becnhmarking succesful, assert success/True.
-        assert True
-    except Exception as e:
-        # If anything happen during benchmarking, assert False/failure.
-        assert False