Update requirements.txt

* 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

@@ -2,4 +2,4 @@
 count = 1
 show-source = 1
 select = E9,F63,F7,F82
-exclude = lit.cfg.py
+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

@@ -50,12 +50,13 @@ jobs:
       shell: powershell
       run: |
         ./setup_venv.ps1
-        $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
-        pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
         python process_skipfiles.py
-        pyinstaller .\apps\stable_diffusion\shark_sd.spec
-        mv ./dist/shark_sd.exe ./dist/nodai_shark_sd_${{ env.package_version_ }}.exe
-        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/nodai_shark_sd_${{ env.package_version_ }}.exe
+        $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
+        pip install -e .
+        pip freeze -l
+        pyinstaller .\apps\shark_studio\shark_studio.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
   
     - name: Upload Release Assets
       id: upload-release-assets
@@ -74,80 +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: ["3.11"]
-        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://openxla.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

@@ -1,159 +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: ["3.11"]
-        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
-    
-    - 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
-          
-    - 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 .
-        # 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 $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
-        source shark.venv/bin/activate
-        pytest --forked --benchmark=native --ci --ci_sha=${SHORT_SHA} --update_tank --tank_url="gs://shark_tank/nightly/" -k cpu 
-        gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${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 }} ./setup_venv.sh
-        source shark.venv/bin/activate
-        pytest --forked --benchmark=native --ci --ci_sha=${SHORT_SHA} --update_tank --tank_url="gs://shark_tank/nightly/" -k cuda
-        gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${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" --tank_url="gs://shark_tank/nightly/" -k vulkan
-
-    - 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="native" --ci --ci_sha=${SHORT_SHA} --update_tank --tank_url="gs://shark_tank/nightly/" -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 -k vulkan -s --ci
-
-    - name: Validate Stable Diffusion Models (Windows)
-      if: matrix.suite == 'vulkan' && matrix.os == '7950x'
-      run: |
-        ./setup_venv.ps1
-        python process_skipfiles.py
-        pyinstaller .\apps\stable_diffusion\shark_sd.spec
-        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
@@ -157,12 +159,12 @@ 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
@@ -170,6 +172,7 @@ shark_tmp/
 tank/dict_configs.py
 *.csv
 reproducers/
+apps/shark_studio/web/configs
 
 # ORT related artefacts
 cache_models/
@@ -180,10 +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

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 the latest driver [here](https://www.amd.com/en/support/kb/release-notes/rn-rad-win-23-2-1).
-* [macOS Users] Download and install the 1.3.216 Vulkan SDK from [here](https://sdk.lunarg.com/sdk/download/1.3.216.0/mac/vulkansdk-macos-1.3.216.0.dmg). Newer versions of the SDK will not work. 
+* [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.
 
@@ -22,23 +24,23 @@ 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 the 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 [stable release](https://github.com/nod-ai/shark/releases/latest)
+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/) 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 put them 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.
 
 <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` 
+* 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
 
@@ -46,17 +48,22 @@ Enjoy.
 * 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. 
+* 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
@@ -64,14 +71,22 @@ 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 3.11.x version from [here](https://www.python.org/downloads/windows/)
 
-* Install Git for Windows from [here](https://git-scm.com/download/win)
-
 #### Allow the install script to run in Powershell
 ```powershell
 set-executionpolicy remotesigned
@@ -86,21 +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
@@ -114,7 +128,7 @@ source shark.venv/bin/activate
 
 #### Windows 10/11 Users
 ```powershell
-(shark.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"
+(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
@@ -142,7 +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>
@@ -170,7 +184,7 @@ python -m pip install --upgrade pip
 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.
@@ -205,7 +219,7 @@ python ./minilm_jit.py --device="cpu"  #use cuda or vulkan or metal
 If you want to use Python3.11 and with TF Import tools you can use the environment variables like:
 Set `USE_IREE=1` to use upstream IREE
 ```
-# PYTHON=python3.11 VENV_DIR=0617_venv IMPORTER=1 ./setup_venv.sh 
+# 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
@@ -214,7 +228,7 @@ 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
@@ -240,12 +254,12 @@ 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 pytest 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                                                                                
+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.
 
@@ -254,7 +268,6 @@ if you want to instead incorporate this into a python script, you can pass the `
 ```
 shark_module = SharkInference(
         mlir_model,
-        func_name,
         device=args.device,
         mlir_dialect="tm_tensor",
         dispatch_benchmarks="all",
@@ -265,7 +278,7 @@ 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
@@ -297,7 +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))
 
@@ -320,15 +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 |
 |---------------------|----------|----------|-------------|
@@ -354,7 +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
@@ -369,7 +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/language_models/scripts/stablelm.py

@@ -1,210 +0,0 @@
-import torch
-import torch_mlir
-from transformers import (
-    AutoTokenizer,
-    StoppingCriteria,
-)
-from io import BytesIO
-from pathlib import Path
-from apps.language_models.utils import (
-    get_torch_mlir_module_bytecode,
-    get_vmfb_from_path,
-)
-
-
-class StopOnTokens(StoppingCriteria):
-    def __call__(
-        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
-    ) -> bool:
-        stop_ids = [50278, 50279, 50277, 1, 0]
-        for stop_id in stop_ids:
-            if input_ids[0][-1] == stop_id:
-                return True
-        return False
-
-
-def shouldStop(tokens):
-    stop_ids = [50278, 50279, 50277, 1, 0]
-    for stop_id in stop_ids:
-        if tokens[0][-1] == stop_id:
-            return True
-    return False
-
-
-MAX_SEQUENCE_LENGTH = 256
-
-
-def user(message, history):
-    # Append the user's message to the conversation history
-    return "", history + [[message, ""]]
-
-
-def compile_stableLM(
-    model,
-    model_inputs,
-    model_name,
-    model_vmfb_name,
-    device="cuda",
-    precision="fp32",
-):
-    from shark.shark_inference import SharkInference
-
-    # device = "cuda"  # "cpu"
-    # TODO: vmfb and mlir name should include precision and device
-    vmfb_path = (
-        Path(model_name + f"_{device}.vmfb")
-        if model_vmfb_name is None
-        else Path(model_vmfb_name)
-    )
-    shark_module = get_vmfb_from_path(
-        vmfb_path, device, mlir_dialect="tm_tensor"
-    )
-    if shark_module is not None:
-        return shark_module
-
-    mlir_path = Path(model_name + ".mlir")
-    print(
-        f"[DEBUG] mlir path {mlir_path} {'exists' if mlir_path.exists() else 'does not exist'}"
-    )
-    if mlir_path.exists():
-        with open(mlir_path, "rb") as f:
-            bytecode = f.read()
-    else:
-        ts_graph = get_torch_mlir_module_bytecode(model, model_inputs)
-        module = torch_mlir.compile(
-            ts_graph,
-            [*model_inputs],
-            torch_mlir.OutputType.LINALG_ON_TENSORS,
-            use_tracing=False,
-            verbose=False,
-        )
-        bytecode_stream = BytesIO()
-        module.operation.write_bytecode(bytecode_stream)
-        bytecode = bytecode_stream.getvalue()
-    f_ = open(model_name + ".mlir", "wb")
-    f_.write(bytecode)
-    print("Saved mlir")
-    f_.close()
-
-    shark_module = SharkInference(
-        mlir_module=bytecode, device=device, mlir_dialect="tm_tensor"
-    )
-    shark_module.compile()
-
-    path = shark_module.save_module(
-        vmfb_path.parent.absolute(), vmfb_path.stem
-    )
-    print("Saved vmfb at ", str(path))
-
-    return shark_module
-
-
-class StableLMModel(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(self, input_ids, attention_mask):
-        combine_input_dict = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-        output = self.model(**combine_input_dict)
-        return output.logits
-
-
-# Initialize a StopOnTokens object
-system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
-- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
-- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
-- StableLM will refuse to participate in anything that could harm a human.
-"""
-
-
-def get_tokenizer():
-    model_path = "stabilityai/stablelm-tuned-alpha-3b"
-    tok = AutoTokenizer.from_pretrained(model_path)
-    tok.add_special_tokens({"pad_token": "<PAD>"})
-    print("Sucessfully loaded the tokenizer to the memory")
-    return tok
-
-
-# sharkStableLM = compile_stableLM
-# (
-#   None,
-#   tuple([input_ids, attention_mask]),
-#   "stableLM_linalg_f32_seqLen256",
-#   "/home/shark/vivek/stableLM_shark_f32_seqLen256"
-# )
-def generate(
-    new_text,
-    max_new_tokens,
-    sharkStableLM,
-    tokenizer=None,
-):
-    if tokenizer is None:
-        tokenizer = get_tokenizer()
-    # Construct the input message string for the model by
-    # concatenating the current system message and conversation history
-    # Tokenize the messages string
-    # sharkStableLM = compile_stableLM
-    # (
-    #   None,
-    #   tuple([input_ids, attention_mask]),
-    #   "stableLM_linalg_f32_seqLen256",
-    #   "/home/shark/vivek/stableLM_shark_f32_seqLen256"
-    # )
-    words_list = []
-    for i in range(max_new_tokens):
-        # numWords = len(new_text.split())
-        # if(numWords>220):
-        #  break
-        params = {
-            "new_text": new_text,
-        }
-        generated_token_op = generate_new_token(
-            sharkStableLM, tokenizer, params
-        )
-        detok = generated_token_op["detok"]
-        stop_generation = generated_token_op["stop_generation"]
-        if stop_generation:
-            break
-        print(detok, end="", flush=True)
-        words_list.append(detok)
-        if detok == "":
-            break
-        new_text = new_text + detok
-    return words_list
-
-
-def generate_new_token(shark_model, tokenizer, params):
-    new_text = params["new_text"]
-    model_inputs = tokenizer(
-        [new_text],
-        padding="max_length",
-        max_length=MAX_SEQUENCE_LENGTH,
-        truncation=True,
-        return_tensors="pt",
-    )
-    sum_attentionmask = torch.sum(model_inputs.attention_mask)
-    # sharkStableLM = compile_stableLM(None, tuple([input_ids, attention_mask]), "stableLM_linalg_f32_seqLen256", "/home/shark/vivek/stableLM_shark_f32_seqLen256")
-    output = shark_model(
-        "forward", [model_inputs.input_ids, model_inputs.attention_mask]
-    )
-    output = torch.from_numpy(output)
-    next_toks = torch.topk(output, 1)
-    stop_generation = False
-    if shouldStop(next_toks.indices):
-        stop_generation = True
-    new_token = next_toks.indices[0][int(sum_attentionmask) - 1]
-    detok = tokenizer.decode(
-        new_token,
-        skip_special_tokens=True,
-    )
-    ret_dict = {
-        "new_token": new_token,
-        "detok": detok,
-        "stop_generation": stop_generation,
-    }
-    return ret_dict

apps/language_models/scripts/vicuna.py

@@ -1,122 +0,0 @@
-import argparse
-from pathlib import Path
-from apps.language_models.src.pipelines import vicuna_pipeline as vp
-from apps.language_models.src.pipelines import vicuna_sharded_pipeline as vsp
-import torch
-
-
-parser = argparse.ArgumentParser(
-    prog="vicuna runner",
-    description="runs a vicuna model",
-)
-
-parser.add_argument(
-    "--precision", "-p", default="fp32", help="fp32, fp16, int8, int4"
-)
-parser.add_argument("--device", "-d", default="cuda", help="vulkan, cpu, cuda")
-parser.add_argument(
-    "--first_vicuna_vmfb_path", default=None, help="path to first vicuna vmfb"
-)
-parser.add_argument(
-    "-s",
-    "--sharded",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="Run model as sharded",
-)
-# TODO: sharded config
-
-parser.add_argument(
-    "--second_vicuna_vmfb_path",
-    default=None,
-    help="path to second vicuna vmfb",
-)
-parser.add_argument(
-    "--first_vicuna_mlir_path",
-    default=None,
-    help="path to first vicuna mlir file",
-)
-parser.add_argument(
-    "--second_vicuna_mlir_path",
-    default=None,
-    help="path to second vicuna mlir",
-)
-
-parser.add_argument(
-    "--load_mlir_from_shark_tank",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="download precompile mlir from shark tank",
-)
-parser.add_argument(
-    "--cli",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="Run model in cli mode",
-)
-
-if __name__ == "__main__":
-    args, unknown = parser.parse_known_args()
-
-    vic = None
-    if not args.sharded:
-        first_vic_mlir_path = (
-            Path(f"first_vicuna_{args.precision}.mlir")
-            if args.first_vicuna_mlir_path is None
-            else Path(args.first_vicuna_mlir_path)
-        )
-        second_vic_mlir_path = (
-            Path(f"second_vicuna_{args.precision}.mlir")
-            if args.second_vicuna_mlir_path is None
-            else Path(args.second_vicuna_mlir_path)
-        )
-        first_vic_vmfb_path = (
-            Path(
-                f"first_vicuna_{args.precision}_{args.device.replace('://', '_')}.vmfb"
-            )
-            if args.first_vicuna_vmfb_path is None
-            else Path(args.first_vicuna_vmfb_path)
-        )
-        second_vic_vmfb_path = (
-            Path(
-                f"second_vicuna_{args.precision}_{args.device.replace('://', '_')}.vmfb"
-            )
-            if args.second_vicuna_vmfb_path is None
-            else Path(args.second_vicuna_vmfb_path)
-        )
-        vic = vp.Vicuna(
-            "vicuna",
-            device=args.device,
-            precision=args.precision,
-            first_vicuna_mlir_path=first_vic_mlir_path,
-            second_vicuna_mlir_path=second_vic_mlir_path,
-            first_vicuna_vmfb_path=first_vic_vmfb_path,
-            second_vicuna_vmfb_path=second_vic_vmfb_path,
-            load_mlir_from_shark_tank=args.load_mlir_from_shark_tank,
-        )
-    else:
-        vic = vsp.Vicuna(
-            "vicuna",
-            device=args.device,
-            precision=args.precision,
-        )
-    prompt_history = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
-    prologue_prompt = "ASSISTANT:\n"
-
-    import gc
-
-    while True:
-        # TODO: Add break condition from user input
-        user_prompt = input("User: ")
-        prompt_history = (
-            prompt_history + "USER:\n" + user_prompt + prologue_prompt
-        )
-        prompt = prompt_history.strip()
-        res_str = vic.generate(prompt, cli=True)
-        torch.cuda.empty_cache()
-        gc.collect()
-        print(
-            "\n-----\nAssistant: Here's the complete formatted reply:\n",
-            res_str,
-        )
-        prompt_history += f"\n{res_str}\n"

apps/language_models/src/model_wrappers/falcon_model.py

@@ -1,22 +0,0 @@
-import torch
-
-
-class FalconModel(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(self, input_ids, attention_mask):
-        input_dict = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-            "past_key_values": None,
-            "use_cache": True,
-        }
-        output = self.model(
-            **input_dict,
-            return_dict=True,
-            output_attentions=False,
-            output_hidden_states=False,
-        )[0]
-        return output[:, -1, :]

apps/language_models/src/model_wrappers/stablelm_model.py

@@ -1,15 +0,0 @@
-import torch
-
-
-class StableLMModel(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(self, input_ids, attention_mask):
-        combine_input_dict = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-        output = self.model(**combine_input_dict)
-        return output.logits

apps/language_models/src/model_wrappers/vicuna_model.py

@@ -1,261 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM
-
-
-class FirstVicuna(torch.nn.Module):
-    def __init__(self, model_path):
-        super().__init__()
-        kwargs = {"torch_dtype": torch.float32}
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_path, low_cpu_mem_usage=True, **kwargs
-        )
-
-    def forward(self, input_ids):
-        op = self.model(input_ids=input_ids, use_cache=True)
-        return_vals = []
-        return_vals.append(op.logits)
-        temp_past_key_values = op.past_key_values
-        for item in temp_past_key_values:
-            return_vals.append(item[0])
-            return_vals.append(item[1])
-        return tuple(return_vals)
-
-
-class SecondVicuna(torch.nn.Module):
-    def __init__(self, model_path):
-        super().__init__()
-        kwargs = {"torch_dtype": torch.float32}
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_path, low_cpu_mem_usage=True, **kwargs
-        )
-
-    def forward(
-        self,
-        i0,
-        i1,
-        i2,
-        i3,
-        i4,
-        i5,
-        i6,
-        i7,
-        i8,
-        i9,
-        i10,
-        i11,
-        i12,
-        i13,
-        i14,
-        i15,
-        i16,
-        i17,
-        i18,
-        i19,
-        i20,
-        i21,
-        i22,
-        i23,
-        i24,
-        i25,
-        i26,
-        i27,
-        i28,
-        i29,
-        i30,
-        i31,
-        i32,
-        i33,
-        i34,
-        i35,
-        i36,
-        i37,
-        i38,
-        i39,
-        i40,
-        i41,
-        i42,
-        i43,
-        i44,
-        i45,
-        i46,
-        i47,
-        i48,
-        i49,
-        i50,
-        i51,
-        i52,
-        i53,
-        i54,
-        i55,
-        i56,
-        i57,
-        i58,
-        i59,
-        i60,
-        i61,
-        i62,
-        i63,
-        i64,
-    ):
-        # input_ids = input_tuple[0]
-        # input_tuple = torch.unbind(pkv, dim=0)
-        token = i0
-        past_key_values = (
-            (i1, i2),
-            (
-                i3,
-                i4,
-            ),
-            (
-                i5,
-                i6,
-            ),
-            (
-                i7,
-                i8,
-            ),
-            (
-                i9,
-                i10,
-            ),
-            (
-                i11,
-                i12,
-            ),
-            (
-                i13,
-                i14,
-            ),
-            (
-                i15,
-                i16,
-            ),
-            (
-                i17,
-                i18,
-            ),
-            (
-                i19,
-                i20,
-            ),
-            (
-                i21,
-                i22,
-            ),
-            (
-                i23,
-                i24,
-            ),
-            (
-                i25,
-                i26,
-            ),
-            (
-                i27,
-                i28,
-            ),
-            (
-                i29,
-                i30,
-            ),
-            (
-                i31,
-                i32,
-            ),
-            (
-                i33,
-                i34,
-            ),
-            (
-                i35,
-                i36,
-            ),
-            (
-                i37,
-                i38,
-            ),
-            (
-                i39,
-                i40,
-            ),
-            (
-                i41,
-                i42,
-            ),
-            (
-                i43,
-                i44,
-            ),
-            (
-                i45,
-                i46,
-            ),
-            (
-                i47,
-                i48,
-            ),
-            (
-                i49,
-                i50,
-            ),
-            (
-                i51,
-                i52,
-            ),
-            (
-                i53,
-                i54,
-            ),
-            (
-                i55,
-                i56,
-            ),
-            (
-                i57,
-                i58,
-            ),
-            (
-                i59,
-                i60,
-            ),
-            (
-                i61,
-                i62,
-            ),
-            (
-                i63,
-                i64,
-            ),
-        )
-        op = self.model(
-            input_ids=token, use_cache=True, past_key_values=past_key_values
-        )
-        return_vals = []
-        return_vals.append(op.logits)
-        temp_past_key_values = op.past_key_values
-        for item in temp_past_key_values:
-            return_vals.append(item[0])
-            return_vals.append(item[1])
-        return tuple(return_vals)
-
-
-class CombinedModel(torch.nn.Module):
-    def __init__(
-        self,
-        first_vicuna_model_path="TheBloke/vicuna-7B-1.1-HF",
-        second_vicuna_model_path="TheBloke/vicuna-7B-1.1-HF",
-    ):
-        super().__init__()
-        self.first_vicuna = FirstVicuna(first_vicuna_model_path)
-        self.second_vicuna = SecondVicuna(second_vicuna_model_path)
-
-    def forward(self, input_ids):
-        first_output = self.first_vicuna(input_ids=input_ids, use_cache=True)
-        logits = first_output[0]
-        pkv = first_output[1:]
-
-        token = torch.argmax(torch.tensor(logits)[:, -1, :], dim=1)
-        token = token.to(torch.int64).reshape([1, 1])
-        secondVicunaInput = (token,) + tuple(pkv)
-        second_output = self.second_vicuna(secondVicunaInput)
-        return second_output

apps/language_models/src/model_wrappers/vicuna_sharded_model.py

@@ -1,178 +0,0 @@
-import torch
-
-
-class FirstVicunaLayer(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(self, hidden_states, attention_mask, position_ids):
-        outputs = self.model(
-            hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            use_cache=True,
-        )
-        next_hidden_states = outputs[0]
-        past_key_value_out0, past_key_value_out1 = (
-            outputs[-1][0],
-            outputs[-1][1],
-        )
-
-        return (
-            next_hidden_states,
-            past_key_value_out0,
-            past_key_value_out1,
-        )
-
-
-class SecondVicunaLayer(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value0,
-        past_key_value1,
-    ):
-        outputs = self.model(
-            hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=(
-                past_key_value0,
-                past_key_value1,
-            ),
-            use_cache=True,
-        )
-        next_hidden_states = outputs[0]
-        past_key_value_out0, past_key_value_out1 = (
-            outputs[-1][0],
-            outputs[-1][1],
-        )
-
-        return (
-            next_hidden_states,
-            past_key_value_out0,
-            past_key_value_out1,
-        )
-
-
-class CompiledFirstVicunaLayer(torch.nn.Module):
-    def __init__(self, shark_module):
-        super().__init__()
-        self.model = shark_module
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value=None,
-        output_attentions=False,
-        use_cache=True,
-    ):
-        hidden_states = hidden_states.detach()
-        attention_mask = attention_mask.detach()
-        position_ids = position_ids.detach()
-        output = self.model(
-            "forward",
-            (
-                hidden_states,
-                attention_mask,
-                position_ids,
-            ),
-        )
-
-        output0 = torch.tensor(output[0])
-        output1 = torch.tensor(output[1])
-        output2 = torch.tensor(output[2])
-
-        return (
-            output0,
-            (
-                output1,
-                output2,
-            ),
-        )
-
-
-class CompiledSecondVicunaLayer(torch.nn.Module):
-    def __init__(self, shark_module):
-        super().__init__()
-        self.model = shark_module
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value,
-        output_attentions=False,
-        use_cache=True,
-    ):
-        hidden_states = hidden_states.detach()
-        attention_mask = attention_mask.detach()
-        position_ids = position_ids.detach()
-        pkv0 = past_key_value[0].detach()
-        pkv1 = past_key_value[1].detach()
-        output = self.model(
-            "forward",
-            (
-                hidden_states,
-                attention_mask,
-                position_ids,
-                pkv0,
-                pkv1,
-            ),
-        )
-
-        output0 = torch.tensor(output[0])
-        output1 = torch.tensor(output[1])
-        output2 = torch.tensor(output[2])
-
-        return (
-            output0,
-            (
-                output1,
-                output2,
-            ),
-        )
-
-
-class ShardedVicunaModel(torch.nn.Module):
-    def __init__(self, model, layers0, layers1):
-        super().__init__()
-        self.model = model
-        assert len(layers0) == len(model.model.layers)
-        # self.model.model.layers = torch.nn.modules.container.ModuleList(layers0)
-        self.model.model.config.use_cache = True
-        self.model.model.config.output_attentions = False
-        self.layers0 = layers0
-        self.layers1 = layers1
-
-    def forward(
-        self,
-        input_ids,
-        is_first=True,
-        past_key_values=None,
-        attention_mask=None,
-    ):
-        if is_first:
-            self.model.model.layers = torch.nn.modules.container.ModuleList(
-                self.layers0
-            )
-            return self.model.forward(input_ids, attention_mask=attention_mask)
-        else:
-            self.model.model.layers = torch.nn.modules.container.ModuleList(
-                self.layers1
-            )
-            return self.model.forward(
-                input_ids,
-                attention_mask=attention_mask,
-                past_key_values=past_key_values,
-            )

apps/language_models/src/pipelines/SharkLLMBase.py

@@ -1,41 +0,0 @@
-from abc import ABC, abstractmethod
-
-
-class SharkLLMBase(ABC):
-    def __init__(
-        self, model_name, hf_model_path=None, max_num_tokens=512
-    ) -> None:
-        self.model_name = model_name
-        self.hf_model_path = hf_model_path
-        self.max_num_tokens = max_num_tokens
-        self.shark_model = None
-        self.device = "cpu"
-        self.precision = "fp32"
-
-    @classmethod
-    @abstractmethod
-    def compile(self):
-        pass
-
-    @classmethod
-    @abstractmethod
-    def generate(self, prompt):
-        pass
-
-    @classmethod
-    @abstractmethod
-    def generate_new_token(self, params):
-        pass
-
-    @classmethod
-    @abstractmethod
-    def get_tokenizer(self):
-        pass
-
-    @classmethod
-    @abstractmethod
-    def get_src_model(self):
-        pass
-
-    def load_init_from_config(self):
-        pass

apps/language_models/src/pipelines/falcon_pipeline.py

@@ -1,504 +0,0 @@
-from apps.language_models.src.model_wrappers.falcon_model import FalconModel
-from apps.language_models.src.pipelines.SharkLLMBase import SharkLLMBase
-from apps.language_models.utils import (
-    get_vmfb_from_path,
-)
-from io import BytesIO
-from pathlib import Path
-from contextlib import redirect_stdout
-from shark.shark_downloader import download_public_file
-from shark.shark_importer import import_with_fx
-from shark.shark_inference import SharkInference
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from transformers.generation import (
-    GenerationConfig,
-    LogitsProcessorList,
-    StoppingCriteriaList,
-)
-import copy
-
-import re
-import torch
-import torch_mlir
-import os
-import argparse
-
-parser = argparse.ArgumentParser(
-    prog="falcon runner",
-    description="runs a falcon model",
-)
-
-parser.add_argument("--falcon_variant_to_use", default="7b", help="7b, 40b")
-parser.add_argument(
-    "--precision", "-p", default="fp32", help="fp32, fp16, int8, int4"
-)
-parser.add_argument("--device", "-d", default="cuda", help="vulkan, cpu, cuda")
-parser.add_argument(
-    "--falcon_vmfb_path", default=None, help="path to falcon's vmfb"
-)
-parser.add_argument(
-    "--falcon_mlir_path",
-    default=None,
-    help="path to falcon's mlir file",
-)
-parser.add_argument(
-    "--use_precompiled_model",
-    default=True,
-    action=argparse.BooleanOptionalAction,
-    help="use the precompiled vmfb",
-)
-parser.add_argument(
-    "--load_mlir_from_shark_tank",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="download precompile mlir from shark tank",
-)
-parser.add_argument(
-    "--cli",
-    default=True,
-    action=argparse.BooleanOptionalAction,
-    help="Run model in cli mode",
-)
-
-
-class Falcon(SharkLLMBase):
-    def __init__(
-        self,
-        model_name,
-        hf_model_path,
-        max_num_tokens=150,
-        device="cuda",
-        precision="fp32",
-        falcon_mlir_path=None,
-        falcon_vmfb_path=None,
-    ) -> None:
-        super().__init__(model_name, hf_model_path, max_num_tokens)
-        self.max_padding_length = 100
-        self.device = device
-        self.precision = precision
-        self.falcon_vmfb_path = falcon_vmfb_path
-        self.falcon_mlir_path = falcon_mlir_path
-        self.tokenizer = self.get_tokenizer()
-        self.shark_model = self.compile()
-        self.src_model = self.get_src_model()
-
-    def get_tokenizer(self):
-        tokenizer = AutoTokenizer.from_pretrained(
-            self.hf_model_path, trust_remote_code=True
-        )
-        tokenizer.padding_side = "left"
-        tokenizer.pad_token_id = 11
-        return tokenizer
-
-    def get_src_model(self):
-        print("Loading src model: ", self.model_name)
-        kwargs = {"torch_dtype": torch.float, "trust_remote_code": True}
-        falcon_model = AutoModelForCausalLM.from_pretrained(
-            self.hf_model_path, **kwargs
-        )
-        return falcon_model
-
-    def compile_falcon(self):
-        if args.use_precompiled_model:
-            if not self.falcon_vmfb_path.exists():
-                # Downloading VMFB from shark_tank
-                download_public_file(
-                    "gs://shark_tank/falcon/"
-                    + "falcon_"
-                    + args.falcon_variant_to_use
-                    + "_"
-                    + self.precision
-                    + "_"
-                    + self.device
-                    + ".vmfb",
-                    self.falcon_vmfb_path.absolute(),
-                    single_file=True,
-                )
-            vmfb = get_vmfb_from_path(
-                self.falcon_vmfb_path, self.device, "linalg"
-            )
-            if vmfb is not None:
-                return vmfb
-
-        print(
-            f"[DEBUG] vmfb not found at {self.falcon_vmfb_path.absolute()}. Trying to work with"
-            f"[DEBUG] mlir path { self.falcon_mlir_path} {'exists' if self.falcon_mlir_path.exists() else 'does not exist'}"
-        )
-        if self.falcon_mlir_path.exists():
-            with open(self.falcon_mlir_path, "rb") as f:
-                bytecode = f.read()
-        else:
-            mlir_generated = False
-            # Downloading MLIR from shark_tank
-            download_public_file(
-                "gs://shark_tank/falcon/"
-                + "falcon_"
-                + args.falcon_variant_to_use
-                + "_"
-                + self.precision
-                + ".mlir",
-                self.falcon_mlir_path.absolute(),
-                single_file=True,
-            )
-            if self.falcon_mlir_path.exists():
-                with open(self.falcon_mlir_path, "rb") as f:
-                    bytecode = f.read()
-                mlir_generated = True
-            else:
-                raise ValueError(
-                    f"MLIR not found at {self.falcon_mlir_path.absolute()}"
-                    " after downloading! Please check path and try again"
-                )
-
-            if not mlir_generated:
-                compilation_input_ids = torch.randint(
-                    low=1, high=10000, size=(1, 100)
-                )
-                compilation_attention_mask = torch.ones(
-                    1, 100, dtype=torch.int64
-                )
-                falconCompileInput = (
-                    compilation_input_ids,
-                    compilation_attention_mask,
-                )
-                model = FalconModel(self.src_model)
-
-                print(f"[DEBUG] generating torchscript graph")
-                ts_graph = import_with_fx(
-                    model,
-                    falconCompileInput,
-                    is_f16=self.precision == "fp16",
-                    f16_input_mask=[False, False],
-                    mlir_type="torchscript",
-                )
-                del model
-                print(f"[DEBUG] generating torch mlir")
-
-                module = torch_mlir.compile(
-                    ts_graph,
-                    [*falconCompileInput],
-                    torch_mlir.OutputType.LINALG_ON_TENSORS,
-                    use_tracing=False,
-                    verbose=False,
-                )
-                del ts_graph
-
-                print(f"[DEBUG] converting to bytecode")
-                bytecode_stream = BytesIO()
-                module.operation.write_bytecode(bytecode_stream)
-                bytecode = bytecode_stream.getvalue()
-                del module
-
-                print(f"[DEBUG] writing mlir to file")
-                with open(f"{self.model_name}.mlir", "wb") as f_:
-                    with redirect_stdout(f_):
-                        print(module.operation.get_asm())
-                f_.close()
-
-        shark_module = SharkInference(
-            mlir_module=bytecode, device=self.device, mlir_dialect="linalg"
-        )
-        path = shark_module.save_module(
-            self.falcon_vmfb_path.parent.absolute(),
-            self.falcon_vmfb_path.stem,
-            extra_args=[
-                "--iree-hal-dump-executable-sources-to=ies",
-                "--iree-vm-target-truncate-unsupported-floats",
-                "--iree-codegen-check-ir-before-llvm-conversion=false",
-                "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-                "--iree-spirv-index-bits=64",
-            ],
-        )
-        print("Saved falcon vmfb at ", str(path))
-        shark_module.load_module(path)
-
-        return shark_module
-
-    def compile(self):
-        falcon_shark_model = self.compile_falcon()
-        return falcon_shark_model
-
-    def generate(self, prompt):
-        model_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.max_padding_length,
-            add_special_tokens=False,
-            return_tensors="pt",
-        )
-        model_inputs["prompt_text"] = prompt
-
-        input_ids = model_inputs["input_ids"]
-        attention_mask = model_inputs.get("attention_mask", None)
-
-        # Allow empty prompts
-        if input_ids.shape[1] == 0:
-            input_ids = None
-            attention_mask = None
-            in_b = 1
-        else:
-            in_b = input_ids.shape[0]
-
-        generate_kwargs = {
-            "max_length": self.max_num_tokens,
-            "do_sample": True,
-            "top_k": 10,
-            "num_return_sequences": 1,
-            "eos_token_id": 11,
-        }
-        generate_kwargs["input_ids"] = input_ids
-        generate_kwargs["attention_mask"] = attention_mask
-        generation_config_ = GenerationConfig.from_model_config(
-            self.src_model.config
-        )
-        generation_config = copy.deepcopy(generation_config_)
-        model_kwargs = generation_config.update(**generate_kwargs)
-
-        logits_processor = LogitsProcessorList()
-        stopping_criteria = StoppingCriteriaList()
-
-        eos_token_id = generation_config.eos_token_id
-        generation_config.pad_token_id = eos_token_id
-
-        (
-            inputs_tensor,
-            model_input_name,
-            model_kwargs,
-        ) = self.src_model._prepare_model_inputs(
-            None, generation_config.bos_token_id, model_kwargs
-        )
-        batch_size = inputs_tensor.shape[0]
-
-        model_kwargs["output_attentions"] = generation_config.output_attentions
-        model_kwargs[
-            "output_hidden_states"
-        ] = generation_config.output_hidden_states
-        model_kwargs["use_cache"] = generation_config.use_cache
-
-        input_ids = (
-            inputs_tensor
-            if model_input_name == "input_ids"
-            else model_kwargs.pop("input_ids")
-        )
-
-        self.logits_processor = self.src_model._get_logits_processor(
-            generation_config=generation_config,
-            input_ids_seq_length=input_ids.shape[-1],
-            encoder_input_ids=inputs_tensor,
-            prefix_allowed_tokens_fn=None,
-            logits_processor=logits_processor,
-        )
-
-        self.stopping_criteria = self.src_model._get_stopping_criteria(
-            generation_config=generation_config,
-            stopping_criteria=stopping_criteria,
-        )
-
-        self.logits_warper = self.src_model._get_logits_warper(
-            generation_config
-        )
-
-        (
-            self.input_ids,
-            self.model_kwargs,
-        ) = self.src_model._expand_inputs_for_generation(
-            input_ids=input_ids,
-            expand_size=generation_config.num_return_sequences,  # 1
-            is_encoder_decoder=self.src_model.config.is_encoder_decoder,  # False
-            **model_kwargs,
-        )
-
-        if isinstance(eos_token_id, int):
-            eos_token_id = [eos_token_id]
-        self.eos_token_id_tensor = (
-            torch.tensor(eos_token_id) if eos_token_id is not None else None
-        )
-
-        self.pad_token_id = generation_config.pad_token_id
-        self.eos_token_id = eos_token_id
-
-        output_scores = generation_config.output_scores  # False
-        output_attentions = generation_config.output_attentions  # False
-        output_hidden_states = generation_config.output_hidden_states  # False
-        return_dict_in_generate = (
-            generation_config.return_dict_in_generate  # False
-        )
-
-        # init attention / hidden states / scores tuples
-        self.scores = (
-            () if (return_dict_in_generate and output_scores) else None
-        )
-        decoder_attentions = (
-            () if (return_dict_in_generate and output_attentions) else None
-        )
-        cross_attentions = (
-            () if (return_dict_in_generate and output_attentions) else None
-        )
-        decoder_hidden_states = (
-            () if (return_dict_in_generate and output_hidden_states) else None
-        )
-
-        # keep track of which sequences are already finished
-        self.unfinished_sequences = torch.ones(
-            input_ids.shape[0], dtype=torch.long, device=input_ids.device
-        )
-
-        all_text = prompt
-
-        for i in range(self.max_num_tokens - 1):
-            next_token = self.generate_new_token()
-            new_word = self.tokenizer.decode(
-                next_token.cpu().numpy(),
-                add_special_tokens=False,
-                skip_special_tokens=True,
-                clean_up_tokenization_spaces=True,
-            )
-
-            all_text = all_text + new_word
-
-            print(f"{new_word}", end="", flush=True)
-
-            # if eos_token was found in one sentence, set sentence to finished
-            if self.eos_token_id_tensor is not None:
-                self.unfinished_sequences = self.unfinished_sequences.mul(
-                    next_token.tile(self.eos_token_id_tensor.shape[0], 1)
-                    .ne(self.eos_token_id_tensor.unsqueeze(1))
-                    .prod(dim=0)
-                )
-                # stop when each sentence is finished
-                if (
-                    self.unfinished_sequences.max() == 0
-                    or self.stopping_criteria(input_ids, self.scores)
-                ):
-                    break
-
-        torch.cuda.empty_cache()
-        gc.collect()
-
-        return all_text
-
-    def generate_new_token(self):
-        model_inputs = self.src_model.prepare_inputs_for_generation(
-            self.input_ids, **self.model_kwargs
-        )
-        outputs = torch.from_numpy(
-            self.shark_model(
-                "forward",
-                (model_inputs["input_ids"], model_inputs["attention_mask"]),
-            )
-        )
-        if self.precision == "fp16":
-            outputs = outputs.to(dtype=torch.float32)
-        next_token_logits = outputs
-
-        # pre-process distribution
-        next_token_scores = self.logits_processor(
-            self.input_ids, next_token_logits
-        )
-        next_token_scores = self.logits_warper(
-            self.input_ids, next_token_scores
-        )
-
-        # sample
-        probs = torch.nn.functional.softmax(next_token_scores, dim=-1)
-
-        next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
-
-        # finished sentences should have their next token be a padding token
-        if self.eos_token_id is not None:
-            if self.pad_token_id is None:
-                raise ValueError(
-                    "If `eos_token_id` is defined, make sure that `pad_token_id` is defined."
-                )
-            next_token = (
-                next_token * self.unfinished_sequences
-                + self.pad_token_id * (1 - self.unfinished_sequences)
-            )
-
-        self.input_ids = torch.cat(
-            [self.input_ids, next_token[:, None]], dim=-1
-        )
-
-        self.model_kwargs["past_key_values"] = None
-        if "attention_mask" in self.model_kwargs:
-            attention_mask = self.model_kwargs["attention_mask"]
-            self.model_kwargs["attention_mask"] = torch.cat(
-                [
-                    attention_mask,
-                    attention_mask.new_ones((attention_mask.shape[0], 1)),
-                ],
-                dim=-1,
-            )
-
-        self.input_ids = self.input_ids[:, 1:]
-        self.model_kwargs["attention_mask"] = self.model_kwargs[
-            "attention_mask"
-        ][:, 1:]
-
-        return next_token
-
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-
-    falcon_mlir_path = (
-        Path(
-            "falcon_"
-            + args.falcon_variant_to_use
-            + "_"
-            + args.precision
-            + ".mlir"
-        )
-        if args.falcon_mlir_path is None
-        else Path(args.falcon_mlir_path)
-    )
-    falcon_vmfb_path = (
-        Path(
-            "falcon_"
-            + args.falcon_variant_to_use
-            + "_"
-            + args.precision
-            + "_"
-            + args.device
-            + ".vmfb"
-        )
-        if args.falcon_vmfb_path is None
-        else Path(args.falcon_vmfb_path)
-    )
-
-    falcon = Falcon(
-        "falcon_" + args.falcon_variant_to_use,
-        hf_model_path="tiiuae/falcon-"
-        + args.falcon_variant_to_use
-        + "-instruct",
-        device=args.device,
-        precision=args.precision,
-        falcon_mlir_path=falcon_mlir_path,
-        falcon_vmfb_path=falcon_vmfb_path,
-    )
-
-    import gc
-
-    default_prompt_text = "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:"
-    continue_execution = True
-
-    while continue_execution:
-        use_default_prompt = input(
-            "\nDo you wish to use the default prompt text? True or False?: "
-        )
-        if use_default_prompt:
-            prompt = default_prompt_text
-        else:
-            prompt = input("Please enter the prompt text: ")
-        print("\nPrompt Text: ", prompt)
-
-        res_str = falcon.generate(prompt)
-        torch.cuda.empty_cache()
-        gc.collect()
-        print(
-            "\n\n-----\nHere's the complete formatted result: \n\n",
-            res_str,
-        )
-        continue_execution = input(
-            "\nDo you wish to run script one more time? True or False?: "
-        )

apps/language_models/src/pipelines/stablelm_pipeline.py

@@ -1,185 +0,0 @@
-import torch
-import torch_mlir
-from transformers import AutoTokenizer, StoppingCriteria, AutoModelForCausalLM
-from io import BytesIO
-from pathlib import Path
-from apps.language_models.utils import (
-    get_torch_mlir_module_bytecode,
-    get_vmfb_from_path,
-)
-from apps.language_models.src.pipelines.SharkLLMBase import SharkLLMBase
-from apps.language_models.src.model_wrappers.stablelm_model import (
-    StableLMModel,
-)
-
-
-class StopOnTokens(StoppingCriteria):
-    def __call__(
-        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
-    ) -> bool:
-        stop_ids = [50278, 50279, 50277, 1, 0]
-        for stop_id in stop_ids:
-            if input_ids[0][-1] == stop_id:
-                return True
-        return False
-
-
-class SharkStableLM(SharkLLMBase):
-    def __init__(
-        self,
-        model_name,
-        hf_model_path="stabilityai/stablelm-tuned-alpha-3b",
-        max_num_tokens=512,
-        device="cuda",
-        precision="fp32",
-    ) -> None:
-        super().__init__(model_name, hf_model_path, max_num_tokens)
-        self.max_sequence_len = 256
-        self.device = device
-        self.precision = precision
-        self.tokenizer = self.get_tokenizer()
-        self.shark_model = self.compile()
-
-    def shouldStop(self, tokens):
-        stop_ids = [50278, 50279, 50277, 1, 0]
-        for stop_id in stop_ids:
-            if tokens[0][-1] == stop_id:
-                return True
-        return False
-
-    def get_src_model(self):
-        model = AutoModelForCausalLM.from_pretrained(
-            self.hf_model_path, torch_dtype=torch.float32
-        )
-        return model
-
-    def get_model_inputs(self):
-        input_ids = torch.randint(3, (1, self.max_sequence_len))
-        attention_mask = torch.randint(3, (1, self.max_sequence_len))
-        return input_ids, attention_mask
-
-    def compile(self):
-        tmp_model_name = (
-            f"stableLM_linalg_{self.precision}_seqLen{self.max_sequence_len}"
-        )
-
-        # device = "cuda"  # "cpu"
-        # TODO: vmfb and mlir name should include precision and device
-        model_vmfb_name = None
-        vmfb_path = (
-            Path(tmp_model_name + f"_{self.device}.vmfb")
-            if model_vmfb_name is None
-            else Path(model_vmfb_name)
-        )
-        shark_module = get_vmfb_from_path(
-            vmfb_path, self.device, mlir_dialect="tm_tensor"
-        )
-        if shark_module is not None:
-            return shark_module
-
-        mlir_path = Path(tmp_model_name + ".mlir")
-        print(
-            f"[DEBUG] mlir path {mlir_path} {'exists' if mlir_path.exists() else 'does not exist'}"
-        )
-        if mlir_path.exists():
-            with open(mlir_path, "rb") as f:
-                bytecode = f.read()
-        else:
-            model = StableLMModel(self.get_src_model())
-            model_inputs = self.get_model_inputs()
-            ts_graph = get_torch_mlir_module_bytecode(model, model_inputs)
-            module = torch_mlir.compile(
-                ts_graph,
-                [*model_inputs],
-                torch_mlir.OutputType.LINALG_ON_TENSORS,
-                use_tracing=False,
-                verbose=False,
-            )
-            bytecode_stream = BytesIO()
-            module.operation.write_bytecode(bytecode_stream)
-            bytecode = bytecode_stream.getvalue()
-        f_ = open(tmp_model_name + ".mlir", "wb")
-        f_.write(bytecode)
-        print("Saved mlir")
-        f_.close()
-
-        from shark.shark_inference import SharkInference
-
-        shark_module = SharkInference(
-            mlir_module=bytecode, device=self.device, mlir_dialect="tm_tensor"
-        )
-        shark_module.compile()
-
-        path = shark_module.save_module(
-            vmfb_path.parent.absolute(), vmfb_path.stem
-        )
-        print("Saved vmfb at ", str(path))
-
-        return shark_module
-
-    def get_tokenizer(self):
-        tok = AutoTokenizer.from_pretrained(self.hf_model_path)
-        tok.add_special_tokens({"pad_token": "<PAD>"})
-        # print("[DEBUG] Sucessfully loaded the tokenizer to the memory")
-        return tok
-
-    def generate(self, prompt):
-        words_list = []
-        for i in range(self.max_num_tokens):
-            params = {
-                "new_text": prompt,
-            }
-
-            generated_token_op = self.generate_new_token(params)
-
-            detok = generated_token_op["detok"]
-            stop_generation = generated_token_op["stop_generation"]
-
-            if stop_generation:
-                break
-
-            print(detok, end="", flush=True)  # this is for CLI and DEBUG
-            words_list.append(detok)
-            if detok == "":
-                break
-            prompt = prompt + detok
-        return words_list
-
-    def generate_new_token(self, params):
-        new_text = params["new_text"]
-        model_inputs = self.tokenizer(
-            [new_text],
-            padding="max_length",
-            max_length=self.max_sequence_len,
-            truncation=True,
-            return_tensors="pt",
-        )
-        sum_attentionmask = torch.sum(model_inputs.attention_mask)
-        output = self.shark_model(
-            "forward", [model_inputs.input_ids, model_inputs.attention_mask]
-        )
-        output = torch.from_numpy(output)
-        next_toks = torch.topk(output, 1)
-        stop_generation = False
-        if self.shouldStop(next_toks.indices):
-            stop_generation = True
-        new_token = next_toks.indices[0][int(sum_attentionmask) - 1]
-        detok = self.tokenizer.decode(
-            new_token,
-            skip_special_tokens=True,
-        )
-        ret_dict = {
-            "new_token": new_token,
-            "detok": detok,
-            "stop_generation": stop_generation,
-        }
-        return ret_dict
-
-
-# Initialize a StopOnTokens object
-system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
-- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
-- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
-- StableLM will refuse to participate in anything that could harm a human.
-"""

apps/language_models/src/pipelines/vicuna_pipeline.py

@@ -1,559 +0,0 @@
-from apps.language_models.src.model_wrappers.vicuna_model import (
-    FirstVicuna,
-    SecondVicuna,
-)
-from apps.language_models.src.pipelines.SharkLLMBase import SharkLLMBase
-from apps.language_models.utils import (
-    get_vmfb_from_path,
-)
-
-from io import BytesIO
-from pathlib import Path
-from shark.shark_downloader import download_public_file
-from shark.shark_importer import import_with_fx
-from shark.shark_inference import SharkInference
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-import re
-import torch
-import torch_mlir
-import os
-
-
-class Vicuna(SharkLLMBase):
-    def __init__(
-        self,
-        model_name,
-        hf_model_path="TheBloke/vicuna-7B-1.1-HF",
-        max_num_tokens=512,
-        device="cuda",
-        precision="fp32",
-        first_vicuna_mlir_path=Path("first_vicuna.mlir"),
-        second_vicuna_mlir_path=Path("second_vicuna.mlir"),
-        first_vicuna_vmfb_path=Path("first_vicuna.vmfb"),
-        second_vicuna_vmfb_path=Path("second_vicuna.vmfb"),
-        load_mlir_from_shark_tank=True,
-    ) -> None:
-        super().__init__(model_name, hf_model_path, max_num_tokens)
-        self.max_sequence_length = 256
-        self.device = device
-        self.precision = precision
-        self.first_vicuna_vmfb_path = first_vicuna_vmfb_path
-        self.second_vicuna_vmfb_path = second_vicuna_vmfb_path
-        self.first_vicuna_mlir_path = first_vicuna_mlir_path
-        self.second_vicuna_mlir_path = second_vicuna_mlir_path
-        self.tokenizer = self.get_tokenizer()
-        self.shark_model = self.compile()
-        self.load_mlir_from_shark_tank = load_mlir_from_shark_tank
-
-    def get_tokenizer(self):
-        tokenizer = AutoTokenizer.from_pretrained(
-            self.hf_model_path, use_fast=False
-        )
-        return tokenizer
-
-    def get_src_model(self):
-        kwargs = {"torch_dtype": torch.float}
-        vicuna_model = AutoModelForCausalLM.from_pretrained(
-            self.hf_model_path, **kwargs
-        )
-        return vicuna_model
-
-    def compile_first_vicuna(self):
-        vmfb = get_vmfb_from_path(
-            self.first_vicuna_vmfb_path, self.device, "tm_tensor"
-        )
-        if vmfb is not None:
-            return vmfb
-
-        # Compilation path needs some more work before it is functional
-
-        print(
-            f"[DEBUG] vmfb not found at {self.first_vicuna_vmfb_path.absolute()}. Trying to work with"
-            f"[DEBUG] mlir path { self.first_vicuna_mlir_path} {'exists' if self.first_vicuna_mlir_path.exists() else 'does not exist'}"
-        )
-        if self.first_vicuna_mlir_path.exists():
-            with open(self.first_vicuna_mlir_path, "rb") as f:
-                bytecode = f.read()
-        else:
-            mlir_generated = False
-            if self.load_mlir_from_shark_tank:
-                if self.precision == "fp32":
-                    # download MLIR from shark_tank for fp32
-                    download_public_file(
-                        "gs://shark_tank/vicuna/unsharded/mlir/first_vicuna.mlir",
-                        self.first_vicuna_mlir_path.absolute(),
-                        single_file=True,
-                    )
-                    if self.first_vicuna_mlir_path.exists():
-                        with open(self.first_vicuna_mlir_path, "rb") as f:
-                            bytecode = f.read()
-                        mlir_generated = True
-                    else:
-                        raise ValueError(
-                            f"MLIR not found at {self.first_vicuna_mlir_path.absolute()}"
-                            " after downloading! Please check path and try again"
-                        )
-                else:
-                    print(
-                        "Only fp32 mlir added to tank, generating mlir on device."
-                    )
-
-            if not mlir_generated:
-                compilation_prompt = "".join(["0" for _ in range(17)])
-                compilation_input_ids = self.tokenizer(
-                    compilation_prompt
-                ).input_ids
-                compilation_input_ids = torch.tensor(
-                    compilation_input_ids
-                ).reshape([1, 19])
-                firstVicunaCompileInput = (compilation_input_ids,)
-                model = FirstVicuna(self.hf_model_path)
-
-                print(f"[DEBUG] generating torchscript graph")
-                ts_graph = import_with_fx(
-                    model,
-                    firstVicunaCompileInput,
-                    is_f16=self.precision == "fp16",
-                    f16_input_mask=[False, False],
-                    mlir_type="torchscript",
-                )
-                del model
-                print(f"[DEBUG] generating torch mlir")
-
-                firstVicunaCompileInput = list(firstVicunaCompileInput)
-                firstVicunaCompileInput[0] = torch_mlir.TensorPlaceholder.like(
-                    firstVicunaCompileInput[0], dynamic_axes=[1]
-                )
-                firstVicunaCompileInput = tuple(firstVicunaCompileInput)
-                module = torch_mlir.compile(
-                    ts_graph,
-                    [*firstVicunaCompileInput],
-                    torch_mlir.OutputType.LINALG_ON_TENSORS,
-                    use_tracing=False,
-                    verbose=False,
-                )
-                del ts_graph
-
-                def remove_constant_dim(line):
-                    if "19x" in line:
-                        line = re.sub("19x", "?x", line)
-                        line = re.sub(
-                            "tensor.empty\(\)", "tensor.empty(%dim)", line
-                        )
-                    if "tensor.empty" in line and "?x?" in line:
-                        line = re.sub(
-                            "tensor.empty\(%dim\)",
-                            "tensor.empty(%dim, %dim)",
-                            line,
-                        )
-                    if "arith.cmpi" in line:
-                        line = re.sub("c19", "dim", line)
-                    if " 19," in line:
-                        line = re.sub(" 19,", " %dim,", line)
-                    return line
-
-                module = str(module)
-                new_lines = []
-
-                print(f"[DEBUG] rewriting torch_mlir file")
-                for line in module.splitlines():
-                    line = remove_constant_dim(line)
-                    if "%0 = tensor.empty(%dim) : tensor<?xi64>" in line:
-                        new_lines.append(
-                            "%dim = tensor.dim %arg0, %c1 : tensor<1x?xi64>"
-                        )
-                    if (
-                        "%dim = tensor.dim %arg0, %c1 : tensor<1x?xi64>"
-                        in line
-                    ):
-                        continue
-
-                    new_lines.append(line)
-
-                module = "\n".join(new_lines)
-
-                print(f"[DEBUG] converting to bytecode")
-                del new_lines
-                module = module.encode("UTF-8")
-                module = BytesIO(module)
-                bytecode = module.read()
-                del module
-
-                print(f"[DEBUG] writing mlir to file")
-                f_ = open(self.first_vicuna_mlir_path, "wb")
-                f_.write(bytecode)
-                f_.close()
-
-        shark_module = SharkInference(
-            mlir_module=bytecode, device=self.device, mlir_dialect="tm_tensor"
-        )
-        path = shark_module.save_module(
-            self.first_vicuna_vmfb_path.parent.absolute(),
-            self.first_vicuna_vmfb_path.stem,
-            extra_args=[
-                "--iree-hal-dump-executable-sources-to=ies",
-                "--iree-vm-target-truncate-unsupported-floats",
-                "--iree-codegen-check-ir-before-llvm-conversion=false",
-                "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-            ],
-        )
-        print("Saved first vic vmfb at vmfb at ", str(path))
-        shark_module.load_module(path)
-
-        return shark_module
-
-    def compile_second_vicuna(self):
-        vmfb = get_vmfb_from_path(
-            self.second_vicuna_vmfb_path, self.device, "tm_tensor"
-        )
-        if vmfb is not None:
-            return vmfb
-
-        # Compilation path needs some more work before it is functional
-        print(
-            f"[DEBUG] mlir path {self.second_vicuna_mlir_path} {'exists' if self.second_vicuna_mlir_path.exists() else 'does not exist'}"
-        )
-        if self.second_vicuna_mlir_path.exists():
-            with open(self.second_vicuna_mlir_path, "rb") as f:
-                bytecode = f.read()
-        else:
-            mlir_generated = False
-            if self.load_mlir_from_shark_tank:
-                if self.precision == "fp32":
-                    # download MLIR from shark_tank for fp32
-                    download_public_file(
-                        "gs://shark_tank/vicuna/unsharded/mlir/second_vicuna.mlir",
-                        self.second_vicuna_mlir_path.absolute(),
-                        single_file=True,
-                    )
-                    if self.second_vicuna_mlir_path.exists():
-                        with open(self.second_vicuna_mlir_path, "rb") as f:
-                            bytecode = f.read()
-                        mlir_generated = True
-                    else:
-                        raise ValueError(
-                            f"MLIR not found at {self.second_vicuna_mlir_path.absolute()}"
-                            " after downloading! Please check path and try again"
-                        )
-                else:
-                    print(
-                        "Only fp32 mlir added to tank, generating mlir on device."
-                    )
-
-            if not mlir_generated:
-                compilation_input_ids = torch.zeros([1, 1], dtype=torch.int64)
-                pkv = tuple(
-                    (torch.zeros([1, 32, 19, 128], dtype=torch.float32))
-                    for _ in range(64)
-                )
-                secondVicunaCompileInput = (compilation_input_ids,) + pkv
-                model = SecondVicuna(self.hf_model_path)
-                ts_graph = import_with_fx(
-                    model,
-                    secondVicunaCompileInput,
-                    is_f16=self.precision == "fp16",
-                    f16_input_mask=[False, False],
-                    mlir_type="torchscript",
-                )
-                secondVicunaCompileInput = list(secondVicunaCompileInput)
-                for i in range(len(secondVicunaCompileInput)):
-                    if i != 0:
-                        secondVicunaCompileInput[
-                            i
-                        ] = torch_mlir.TensorPlaceholder.like(
-                            secondVicunaCompileInput[i], dynamic_axes=[2]
-                        )
-                secondVicunaCompileInput = tuple(secondVicunaCompileInput)
-                module = torch_mlir.compile(
-                    ts_graph,
-                    [*secondVicunaCompileInput],
-                    torch_mlir.OutputType.LINALG_ON_TENSORS,
-                    use_tracing=False,
-                    verbose=False,
-                )
-
-                def remove_constant_dim(line):
-                    if "c19_i64" in line:
-                        line = re.sub("c19_i64", "dim_i64", line)
-                    if "19x" in line:
-                        line = re.sub("19x", "?x", line)
-                        line = re.sub(
-                            "tensor.empty\(\)", "tensor.empty(%dim)", line
-                        )
-                    if "tensor.empty" in line and "?x?" in line:
-                        line = re.sub(
-                            "tensor.empty\(%dim\)",
-                            "tensor.empty(%dim, %dim)",
-                            line,
-                        )
-                    if "arith.cmpi" in line:
-                        line = re.sub("c19", "dim", line)
-                    if " 19," in line:
-                        line = re.sub(" 19,", " %dim,", line)
-                    if "20x" in line:
-                        line = re.sub("20x", "?x", line)
-                        line = re.sub(
-                            "tensor.empty\(\)", "tensor.empty(%dimp1)", line
-                        )
-                    if " 20," in line:
-                        line = re.sub(" 20,", " %dimp1,", line)
-                    return line
-
-                module_str = str(module)
-                new_lines = []
-
-                for line in module_str.splitlines():
-                    if "%c19_i64 = arith.constant 19 : i64" in line:
-                        new_lines.append("%c2 = arith.constant 2 : index")
-                        new_lines.append(
-                            "%dim_4_int = tensor.dim %arg1, %c2 : tensor<1x32x?x128xf32>"
-                        )
-                        new_lines.append(
-                            "%dim_i64 = arith.index_cast %dim_4_int : index to i64"
-                        )
-                        continue
-                    if "%c2 = arith.constant 2 : index" in line:
-                        continue
-                    if "%c20_i64 = arith.constant 20 : i64" in line:
-                        new_lines.append("%c1_i64 = arith.constant 1 : i64")
-                        new_lines.append(
-                            "%c20_i64 = arith.addi %dim_i64, %c1_i64 : i64"
-                        )
-                        new_lines.append(
-                            "%dimp1 = arith.index_cast %c20_i64 : i64 to index"
-                        )
-                        continue
-                    line = remove_constant_dim(line)
-                    new_lines.append(line)
-
-                module_str = "\n".join(new_lines)
-                bytecode = module_str.encode("UTF-8")
-                bytecode_stream = BytesIO(bytecode)
-                bytecode = bytecode_stream.read()
-                f_ = open(self.second_vicuna_mlir_path, "wb")
-                f_.write(bytecode)
-                f_.close()
-
-        shark_module = SharkInference(
-            mlir_module=bytecode, device=self.device, mlir_dialect="tm_tensor"
-        )
-
-        path = shark_module.save_module(
-            self.second_vicuna_vmfb_path.parent.absolute(),
-            self.second_vicuna_vmfb_path.stem,
-            extra_args=[
-                "--iree-hal-dump-executable-sources-to=ies",
-                "--iree-vm-target-truncate-unsupported-floats",
-                "--iree-codegen-check-ir-before-llvm-conversion=false",
-                "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-            ],
-        )
-        print("Saved vmfb at ", str(path))
-        shark_module.load_module(self.second_vicuna_vmfb_path)
-
-        # self.shark_module = shark_module
-
-        return shark_module
-
-    def compile(self):
-        # Cannot load both the models in the memory at once
-        # due to memory constraints, hence on demand compilation
-        # is being used until the space is enough for both models
-
-        # Testing : DO NOT Download Vmfbs if not found. Modify later
-        # download vmfbs for A100
-        if (
-            not self.first_vicuna_vmfb_path.exists()
-            and self.device == "cuda"
-            and self.precision == "fp32"
-        ):
-            download_public_file(
-                "gs://shark_tank/vicuna/unsharded/first_vicuna.vmfb",
-                self.first_vicuna_vmfb_path.absolute(),
-                single_file=True,
-            )
-        else:
-            # get first vic
-            # TODO: Remove after testing to avoid memory overload
-            # fvic_shark_model = self.compile_first_vicuna()
-            pass
-        if (
-            not self.second_vicuna_vmfb_path.exists()
-            and self.device == "cuda"
-            and self.precision == "fp32"
-        ):
-            download_public_file(
-                "gs://shark_tank/vicuna/unsharded/second_vicuna.vmfb",
-                self.second_vicuna_vmfb_path.absolute(),
-                single_file=True,
-            )
-        else:
-            # get second vic
-            # TODO: Remove after testing to avoid memory overload
-            # svic_shark_model = self.compile_second_vicuna()
-            pass
-
-        # get first vic
-        # fvic_shark_model = self.compile_first_vicuna()
-        # get second vic
-        # svic_shark_model = self.compile_second_vicuna()
-        # return tuple of shark_modules
-        # return fvic_shark_model, svic_shark_model
-        return None
-        # return tuple of shark_modules once mem is supported
-        # return fvic_shark_model, svic_shark_model
-
-    def generate(self, prompt, cli=False):
-        # TODO: refactor for cleaner integration
-        import gc
-
-        res = []
-        res_tokens = []
-        params = {
-            "prompt": prompt,
-            "is_first": True,
-            "fv": self.compile_first_vicuna(),
-        }
-
-        generated_token_op = self.generate_new_token(params=params)
-
-        token = generated_token_op["token"]
-        logits = generated_token_op["logits"]
-        pkv = generated_token_op["pkv"]
-        detok = generated_token_op["detok"]
-
-        res.append(detok)
-        res_tokens.append(token)
-        if cli:
-            print(f"Assistant: {detok}", end=" ", flush=True)
-
-        # Clear First Vic from Memory (main and cuda)
-        del params
-        torch.cuda.empty_cache()
-        gc.collect()
-
-        sec_vic = self.compile_second_vicuna()
-        for _ in range(self.max_num_tokens - 2):
-            params = {
-                "prompt": None,
-                "is_first": False,
-                "logits": logits,
-                "pkv": pkv,
-                "sv": sec_vic,
-            }
-
-            generated_token_op = self.generate_new_token(params=params)
-
-            token = generated_token_op["token"]
-            logits = generated_token_op["logits"]
-            pkv = generated_token_op["pkv"]
-            detok = generated_token_op["detok"]
-
-            if token == 2:
-                break
-            res_tokens.append(token)
-            if detok == "<0x0A>":
-                res.append("\n")
-                if cli:
-                    print("\n", end="", flush=True)
-            else:
-                res.append(detok)
-                if cli:
-                    print(f"{detok}", end=" ", flush=True)
-        del sec_vic, pkv, logits
-        torch.cuda.empty_cache()
-        gc.collect()
-
-        for i in range(len(res_tokens)):
-            if type(res_tokens[i]) != int:
-                res_tokens[i] = int(res_tokens[i][0])
-
-        res_str = self.tokenizer.decode(res_tokens)
-        # print(f"[DEBUG] final output : \n{res_str}")
-        return res_str
-
-    def generate_new_token(self, params, debug=False):
-        def forward_first(first_vic, prompt, cache_outputs=False):
-            input_ids = self.tokenizer(prompt).input_ids
-            input_id_len = len(input_ids)
-            input_ids = torch.tensor(input_ids)
-            input_ids = input_ids.reshape([1, input_id_len])
-            firstVicunaInput = (input_ids,)
-            assert first_vic is not None
-            output_first_vicuna = first_vic("forward", firstVicunaInput)
-            output_first_vicuna_tensor = torch.tensor(output_first_vicuna[1:])
-            logits_first_vicuna = torch.tensor(output_first_vicuna[0])
-            if cache_outputs:
-                torch.save(
-                    logits_first_vicuna, "logits_first_vicuna_tensor.pt"
-                )
-                torch.save(
-                    output_first_vicuna_tensor, "output_first_vicuna_tensor.pt"
-                )
-            token = torch.argmax(
-                torch.tensor(logits_first_vicuna)[:, -1, :], dim=1
-            )
-            return token, logits_first_vicuna, output_first_vicuna_tensor
-
-        def forward_second(sec_vic, inputs=None, load_inputs=False):
-            if inputs is not None:
-                logits = inputs[0]
-                pkv = inputs[1:]
-            elif load_inputs:
-                pkv = torch.load("output_first_vicuna_tensor.pt")
-                pkv = tuple(torch.tensor(x) for x in pkv)
-                logits = torch.load("logits_first_vicuna_tensor.pt")
-            else:
-                print(
-                    "Either inputs must be given, or load_inputs must be true"
-                )
-                return None
-            token = torch.argmax(torch.tensor(logits)[:, -1, :], dim=1)
-            token = token.to(torch.int64).reshape([1, 1])
-            secondVicunaInput = (token,) + tuple(pkv)
-
-            secondVicunaOutput = sec_vic("forward", secondVicunaInput)
-            new_pkv = secondVicunaOutput[1:]
-            new_logits = secondVicunaOutput[0]
-            new_token = torch.argmax(torch.tensor(new_logits)[:, -1, :], dim=1)
-            return new_token, new_logits, new_pkv
-
-        is_first = params["is_first"]
-
-        if is_first:
-            prompt = params["prompt"]
-            fv = params["fv"]
-            token, logits, pkv = forward_first(
-                fv,  # self.shark_model[0],
-                prompt=prompt,
-                cache_outputs=False,
-            )
-        else:
-            _logits = params["logits"]
-            _pkv = params["pkv"]
-            inputs = (_logits,) + tuple(_pkv)
-            sv = params["sv"]
-            token, logits, pkv = forward_second(
-                sv,  # self.shark_model[1],
-                inputs=inputs,
-                load_inputs=False,
-            )
-
-        detok = self.tokenizer.decode(token)
-        if debug:
-            print(
-                f"[DEBUG] is_first: {is_first} |"
-                f" token : {token} | detok : {detok}"
-            )
-        ret_dict = {
-            "token": token,
-            "logits": logits,
-            "pkv": pkv,
-            "detok": detok,
-        }
-        return ret_dict
-
-    def autocomplete(self, prompt):
-        # use First vic alone to complete a story / prompt / sentence.
-        pass

apps/language_models/src/pipelines/vicuna_sharded_pipeline.py

@@ -1,408 +0,0 @@
-from apps.language_models.src.model_wrappers.vicuna_sharded_model import (
-    FirstVicunaLayer,
-    SecondVicunaLayer,
-    CompiledFirstVicunaLayer,
-    CompiledSecondVicunaLayer,
-    ShardedVicunaModel,
-)
-from apps.language_models.src.pipelines.SharkLLMBase import SharkLLMBase
-from shark.shark_importer import import_with_fx
-from io import BytesIO
-from pathlib import Path
-from shark.shark_inference import SharkInference
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from tqdm import tqdm
-from torch_mlir import TensorPlaceholder
-
-
-import re
-import torch
-import torch_mlir
-import os
-
-
-class Vicuna(SharkLLMBase):
-    def __init__(
-        self,
-        model_name,
-        hf_model_path="TheBloke/vicuna-7B-1.1-HF",
-        max_num_tokens=512,
-        device="cuda",
-        precision="fp32",
-    ) -> None:
-        super().__init__(model_name, hf_model_path, max_num_tokens)
-        self.max_sequence_length = 256
-        self.device = device
-        self.precision = precision
-        self.tokenizer = self.get_tokenizer()
-        self.shark_model = self.compile()
-
-    def get_tokenizer(self):
-        tokenizer = AutoTokenizer.from_pretrained(
-            self.hf_model_path, use_fast=False
-        )
-        return tokenizer
-
-    def get_src_model(self):
-        kwargs = {"torch_dtype": torch.float}
-        vicuna_model = AutoModelForCausalLM.from_pretrained(
-            self.hf_model_path, **kwargs
-        )
-        return vicuna_model
-
-    def write_in_dynamic_inputs0(self, module, dynamic_input_size):
-        new_lines = []
-        for line in module.splitlines():
-            line = re.sub(f"{dynamic_input_size}x", "?x", line)
-            if "?x" in line:
-                line = re.sub("tensor.empty\(\)", "tensor.empty(%dim)", line)
-            line = re.sub(f" {dynamic_input_size},", " %dim,", line)
-            if "tensor.empty" in line and "?x?" in line:
-                line = re.sub(
-                    "tensor.empty\(%dim\)", "tensor.empty(%dim, %dim)", line
-                )
-            if "arith.cmpi" in line:
-                line = re.sub(f"c{dynamic_input_size}", "dim", line)
-            new_lines.append(line)
-        new_module = "\n".join(new_lines)
-        return new_module
-
-    def write_in_dynamic_inputs1(self, module, dynamic_input_size):
-        new_lines = []
-        for line in module.splitlines():
-            if "dim_42 =" in line:
-                continue
-            if f"%c{dynamic_input_size}_i64 =" in line:
-                new_lines.append(
-                    "%dim_42 = tensor.dim %arg1, %c3 : tensor<1x1x1x?xf32>"
-                )
-                new_lines.append(
-                    f"%dim_42_i64 = arith.index_cast %dim_42 : index to i64"
-                )
-                continue
-            line = re.sub(f"{dynamic_input_size}x", "?x", line)
-            if "?x" in line:
-                line = re.sub(
-                    "tensor.empty\(\)", "tensor.empty(%dim_42)", line
-                )
-            line = re.sub(f" {dynamic_input_size},", " %dim_42,", line)
-            if "tensor.empty" in line and "?x?" in line:
-                line = re.sub(
-                    "tensor.empty\(%dim_42\)",
-                    "tensor.empty(%dim_42, %dim_42)",
-                    line,
-                )
-            if "arith.cmpi" in line:
-                line = re.sub(f"c{dynamic_input_size}", "dim_42", line)
-            new_lines.append(line)
-        new_module = "\n".join(new_lines)
-        return new_module
-
-    def compile_vicuna_layer(
-        self,
-        vicuna_layer,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value0=None,
-        past_key_value1=None,
-    ):
-        if past_key_value0 is None and past_key_value1 is None:
-            model_inputs = (hidden_states, attention_mask, position_ids)
-        else:
-            model_inputs = (
-                hidden_states,
-                attention_mask,
-                position_ids,
-                past_key_value0,
-                past_key_value1,
-            )
-        mlir_bytecode = import_with_fx(
-            vicuna_layer,
-            model_inputs,
-            is_f16=self.precision == "fp16",
-            f16_input_mask=[False, False],
-            mlir_type="torchscript",
-        )
-        return mlir_bytecode
-
-    def compile_to_vmfb(self, inputs, layers, is_first=True):
-        mlirs, modules = [], []
-        for idx, layer in tqdm(enumerate(layers), desc="Getting mlirs"):
-            if is_first:
-                mlir_path = Path(f"{idx}_0.mlir")
-                vmfb_path = Path(f"{idx}_0.vmfb")
-            else:
-                mlir_path = Path(f"{idx}_1.mlir")
-                vmfb_path = Path(f"{idx}_1.vmfb")
-            if vmfb_path.exists():
-                continue
-            if mlir_path.exists():
-                # print(f"Found layer {idx} mlir")
-                f_ = open(mlir_path, "rb")
-                bytecode = f_.read()
-                f_.close()
-            else:
-                hidden_states_placeholder = TensorPlaceholder.like(
-                    inputs[0], dynamic_axes=[1]
-                )
-                attention_mask_placeholder = TensorPlaceholder.like(
-                    inputs[1], dynamic_axes=[3]
-                )
-                position_ids_placeholder = TensorPlaceholder.like(
-                    inputs[2], dynamic_axes=[1]
-                )
-                if not is_first:
-                    pkv0_placeholder = TensorPlaceholder.like(
-                        inputs[3], dynamic_axes=[2]
-                    )
-                    pkv1_placeholder = TensorPlaceholder.like(
-                        inputs[4], dynamic_axes=[2]
-                    )
-                print(f"Compiling layer {idx} mlir")
-                if is_first:
-                    ts_g = self.compile_vicuna_layer(
-                        layer, inputs[0], inputs[1], inputs[2]
-                    )
-                    module = torch_mlir.compile(
-                        ts_g,
-                        (
-                            hidden_states_placeholder,
-                            inputs[1],
-                            inputs[2],
-                        ),
-                        torch_mlir.OutputType.LINALG_ON_TENSORS,
-                        use_tracing=False,
-                        verbose=False,
-                    )
-                else:
-                    ts_g = self.compile_vicuna_layer(
-                        layer,
-                        inputs[0],
-                        inputs[1],
-                        inputs[2],
-                        inputs[3],
-                        inputs[4],
-                    )
-                    module = torch_mlir.compile(
-                        ts_g,
-                        (
-                            inputs[0],
-                            attention_mask_placeholder,
-                            inputs[2],
-                            pkv0_placeholder,
-                            pkv1_placeholder,
-                        ),
-                        torch_mlir.OutputType.LINALG_ON_TENSORS,
-                        use_tracing=False,
-                        verbose=False,
-                    )
-
-                # bytecode_stream = BytesIO()
-                # module.operation.write_bytecode(bytecode_stream)
-                # bytecode = bytecode_stream.getvalue()
-
-                if is_first:
-                    module = self.write_in_dynamic_inputs0(str(module), 137)
-                    bytecode = module.encode("UTF-8")
-                    bytecode_stream = BytesIO(bytecode)
-                    bytecode = bytecode_stream.read()
-
-                else:
-                    module = self.write_in_dynamic_inputs1(str(module), 138)
-                    if idx in [0, 5, 6, 7]:
-                        module_str = module
-                        module_str = module_str.splitlines()
-                        new_lines = []
-                        for line in module_str:
-                            if len(line) < 1000:
-                                new_lines.append(line)
-                            else:
-                                new_lines.append(line[:999])
-                        module_str = "\n".join(new_lines)
-                        f1_ = open(f"{idx}_1_test.mlir", "w+")
-                        f1_.write(module_str)
-                        f1_.close()
-
-                    bytecode = module.encode("UTF-8")
-                    bytecode_stream = BytesIO(bytecode)
-                    bytecode = bytecode_stream.read()
-
-                f_ = open(mlir_path, "wb")
-                f_.write(bytecode)
-                f_.close()
-            mlirs.append(bytecode)
-
-        for idx, layer in tqdm(enumerate(layers), desc="compiling modules"):
-            if is_first:
-                vmfb_path = Path(f"{idx}_0.vmfb")
-                if idx < 25:
-                    device = "cpu"
-                else:
-                    device = "cpu"
-                if vmfb_path.exists():
-                    # print(f"Found layer {idx} vmfb")
-                    module = SharkInference(
-                        None, device=device, mlir_dialect="tm_tensor"
-                    )
-                    module.load_module(vmfb_path)
-                else:
-                    print(f"Compiling layer {idx} vmfb")
-                    module = SharkInference(
-                        mlirs[idx], device=device, mlir_dialect="tm_tensor"
-                    )
-                    module.save_module(
-                        module_name=f"{idx}_0",
-                        extra_args=[
-                            "--iree-hal-dump-executable-sources-to=ies",
-                            "--iree-vm-target-truncate-unsupported-floats",
-                            "--iree-codegen-check-ir-before-llvm-conversion=false",
-                            "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-                        ],
-                    )
-                    module.load_module(vmfb_path)
-                modules.append(module)
-            else:
-                vmfb_path = Path(f"{idx}_1.vmfb")
-                if idx < 25:
-                    device = "cpu"
-                else:
-                    device = "cpu"
-                if vmfb_path.exists():
-                    # print(f"Found layer {idx} vmfb")
-                    module = SharkInference(
-                        None, device=device, mlir_dialect="tm_tensor"
-                    )
-                    module.load_module(vmfb_path)
-                else:
-                    print(f"Compiling layer {idx} vmfb")
-                    module = SharkInference(
-                        mlirs[idx], device=device, mlir_dialect="tm_tensor"
-                    )
-                    module.save_module(
-                        module_name=f"{idx}_1",
-                        extra_args=[
-                            "--iree-hal-dump-executable-sources-to=ies",
-                            "--iree-vm-target-truncate-unsupported-floats",
-                            "--iree-codegen-check-ir-before-llvm-conversion=false",
-                            "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-                        ],
-                    )
-                    module.load_module(vmfb_path)
-                modules.append(module)
-
-        return mlirs, modules
-
-    def get_sharded_model(self):
-        # SAMPLE_INPUT_LEN is used for creating mlir with dynamic inputs, which is currently an increadibly hacky proccess
-        # please don't change it
-        SAMPLE_INPUT_LEN = 137
-        vicuna_model = self.get_src_model()
-        placeholder_input0 = (
-            torch.zeros([1, SAMPLE_INPUT_LEN, 4096]),
-            torch.zeros([1, 1, SAMPLE_INPUT_LEN, SAMPLE_INPUT_LEN]),
-            torch.zeros([1, SAMPLE_INPUT_LEN], dtype=torch.int64),
-        )
-
-        placeholder_input1 = (
-            torch.zeros([1, 1, 4096]),
-            torch.zeros([1, 1, 1, SAMPLE_INPUT_LEN + 1]),
-            torch.zeros([1, 1], dtype=torch.int64),
-            torch.zeros([1, 32, SAMPLE_INPUT_LEN, 128]),
-            torch.zeros([1, 32, SAMPLE_INPUT_LEN, 128]),
-        )
-
-        layers0 = [
-            FirstVicunaLayer(layer) for layer in vicuna_model.model.layers
-        ]
-        _, modules0 = self.compile_to_vmfb(
-            placeholder_input0, layers0, is_first=True
-        )
-        shark_layers0 = [CompiledFirstVicunaLayer(m) for m in modules0]
-
-        layers1 = [
-            SecondVicunaLayer(layer) for layer in vicuna_model.model.layers
-        ]
-        _, modules1 = self.compile_to_vmfb(
-            placeholder_input1, layers1, is_first=False
-        )
-        shark_layers1 = [CompiledSecondVicunaLayer(m) for m in modules1]
-
-        sharded_model = ShardedVicunaModel(
-            vicuna_model, shark_layers0, shark_layers1
-        )
-        return sharded_model
-
-    def compile(self):
-        return self.get_sharded_model()
-
-    def generate(self, prompt, cli=False):
-        # TODO: refactor for cleaner integration
-
-        tokens_generated = []
-        _past_key_values = None
-        _token = None
-        detoks_generated = []
-        for iteration in range(self.max_num_tokens):
-            params = {
-                "prompt": prompt,
-                "is_first": iteration == 0,
-                "token": _token,
-                "past_key_values": _past_key_values,
-            }
-
-            generated_token_op = self.generate_new_token(params=params)
-
-            _token = generated_token_op["token"]
-            _past_key_values = generated_token_op["past_key_values"]
-            _detok = generated_token_op["detok"]
-
-            if _token == 2:
-                break
-            detoks_generated.append(_detok)
-            tokens_generated.append(_token)
-
-        for i in range(len(tokens_generated)):
-            if type(tokens_generated[i]) != int:
-                tokens_generated[i] = int(tokens_generated[i][0])
-        result_output = self.tokenizer.decode(tokens_generated)
-        return result_output
-
-    def generate_new_token(self, params):
-        is_first = params["is_first"]
-        if is_first:
-            prompt = params["prompt"]
-            input_ids = self.tokenizer(prompt).input_ids
-            input_id_len = len(input_ids)
-            input_ids = torch.tensor(input_ids)
-            input_ids = input_ids.reshape([1, input_id_len])
-            output = self.shark_model.forward(input_ids, is_first=is_first)
-        else:
-            token = params["token"]
-            past_key_values = params["past_key_values"]
-            input_ids = [token]
-            input_id_len = len(input_ids)
-            input_ids = torch.tensor(input_ids)
-            input_ids = input_ids.reshape([1, input_id_len])
-            output = self.shark_model.forward(
-                input_ids, past_key_values=past_key_values, is_first=is_first
-            )
-
-        _logits = output["logits"]
-        _past_key_values = output["past_key_values"]
-        _token = int(torch.argmax(_logits[:, -1, :], dim=1)[0])
-        _detok = self.tokenizer.decode(_token)
-
-        ret_dict = {
-            "token": _token,
-            "detok": _detok,
-            "past_key_values": _past_key_values,
-        }
-
-        print(f" token : {_token} | detok : {_detok}")
-
-        return ret_dict
-
-    def autocomplete(self, prompt):
-        # use First vic alone to complete a story / prompt / sentence.
-        pass

apps/language_models/utils.py

@@ -1,25 +0,0 @@
-import torch
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch._decomp import get_decompositions
-from typing import List
-from pathlib import Path
-
-
-# expects a Path / str as arg
-# returns None if path not found or SharkInference module
-def get_vmfb_from_path(vmfb_path, device, mlir_dialect):
-    if not isinstance(vmfb_path, Path):
-        vmfb_path = Path(vmfb_path)
-
-    from shark.shark_inference import SharkInference
-
-    if not vmfb_path.exists():
-        return None
-
-    print("Loading vmfb from: ", vmfb_path)
-    shark_module = SharkInference(
-        None, device=device, mlir_dialect=mlir_dialect
-    )
-    shark_module.load_module(vmfb_path)
-    print("Successfully loaded vmfb")
-    return shark_module

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 +1,125 @@
+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")
+
+    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_mlir()
+    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()
+
+    import gradio as gr
+
+    # 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 +1,505 @@
+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 turbine_models.custom_models.sd_inference.sd_pipeline import SharkSDPipeline
+from turbine_models.custom_models.sdxl_inference.sdxl_compiled_pipeline import (
+    SharkSDXLPipeline,
+)
+
+
+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 apps.shark_studio.modules.ckpt_processing import (
+    preprocessCKPT,
+    save_irpa,
+)
+
+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",
+    ):
+        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.model_map = custom_module.MODEL_MAP
+        elif self.is_sdxl:
+            self.turbine_pipe = SharkSDXLPipeline
+            self.model_map = EMPTY_SDXL_MAP
+        else:
+            self.turbine_pipe = SharkSDPipeline
+            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
+
+        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,
+        )
+        print(f"\n[LOG] Pipeline initialized with pipe_id: {self.pipe_id}.")
+        gc.collect()
+
+    def prepare_pipe(
+        self, custom_weights, adapters, embeddings, is_img2img, compiled_pipeline
+    ):
+        print(f"\n[LOG] Preparing pipeline...")
+        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:
+            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.")
+
+                elif key in ["clip", "prompt_encoder"]:
+                    if not self.is_sdxl:
+                        sd1_path = os.path.join(
+                            diffusers_weights_path, "text_encoder", "model.safetensors"
+                        )
+                        weights[key] = save_irpa(sd1_path, "text_encoder_model.")
+                    else:
+                        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 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.")
+
+        vmfbs, weights = self.sd_pipe.check_prepared(
+            mlirs, vmfbs, weights, interactive=False
+        )
+        print(f"\n[LOG] Loading pipeline to device {self.rt_device}.")
+        self.sd_pipe.load_pipeline(
+            vmfbs, weights, self.rt_device, self.compiled_pipeline
+        )
+        print(
+            "\n[LOG] Pipeline successfully prepared for runtime. Generating images..."
+        )
+        return
+
+    def generate_images(
+        self,
+        prompt,
+        negative_prompt,
+        image,
+        strength,
+        guidance_scale,
+        seed,
+        ondemand,
+        resample_type,
+        control_mode,
+        hints,
+    ):
+        img = self.sd_pipe.generate_images(
+            prompt,
+            negative_prompt,
+            1,
+            guidance_scale,
+            seed,
+            return_imgs=True,
+        )
+        return img
+
+
+def shark_sd_fn_dict_input(
+    sd_kwargs: dict,
+):
+    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 == "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 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 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,
+):
+    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 (
+        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 = []
+    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):
+            save_output_img(
+                out_imgs[batch],
+                seed,
+                sd_kwargs,
+            )
+        generated_imgs.extend(out_imgs)
+        # TODO: make seed changes over batch counts more configurable.
+        submit_run_kwargs["seed"] = submit_run_kwargs["seed"] + 1
+        yield generated_imgs, status_label(
+            "Stable Diffusion", current_batch + 1, batch_count, batch_size
+        )
+    return (generated_imgs, "")
+
+
+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, "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)

apps/shark_studio/api/utils.py

@@ -0,0 +1,389 @@
+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
+
+# TODO: migrate these utils to studio
+from shark.iree_utils.vulkan_utils import (
+    set_iree_vulkan_runtime_flags,
+    get_vulkan_target_triple,
+    get_iree_vulkan_runtime_flags,
+)
+
+
+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:
+            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 set_init_device_flags():
+    if "vulkan" in cmd_opts.device:
+        # set runtime flags for vulkan.
+        set_iree_runtime_flags()
+
+        # set triple flag to avoid multiple calls to get_vulkan_triple_flag
+        device_name, cmd_opts.device = map_device_to_name_path(cmd_opts.device)
+        if not cmd_opts.iree_vulkan_target_triple:
+            triple = get_vulkan_target_triple(device_name)
+            if triple is not None:
+                cmd_opts.iree_vulkan_target_triple = triple
+        print(
+            f"Found device {device_name}. Using target triple "
+            f"{cmd_opts.iree_vulkan_target_triple}."
+        )
+    elif "cuda" in cmd_opts.device:
+        cmd_opts.device = "cuda"
+    elif "metal" in cmd_opts.device:
+        device_name, cmd_opts.device = map_device_to_name_path(cmd_opts.device)
+        if not cmd_opts.iree_metal_target_platform:
+            from shark.iree_utils.metal_utils import get_metal_target_triple
+
+            triple = get_metal_target_triple(device_name)
+            if triple is not None:
+                cmd_opts.iree_metal_target_platform = triple.split("-")[-1]
+        print(
+            f"Found device {device_name}. Using target triple "
+            f"{cmd_opts.iree_metal_target_platform}."
+        )
+    elif "cpu" in cmd_opts.device:
+        cmd_opts.device = "cpu"
+
+
+def set_iree_runtime_flags():
+    # TODO: This function should be device-agnostic and piped properly
+    # to general runtime driver init.
+    vulkan_runtime_flags = get_iree_vulkan_runtime_flags()
+    if cmd_opts.enable_rgp:
+        vulkan_runtime_flags += [
+            f"--enable_rgp=true",
+            f"--vulkan_debug_utils=true",
+        ]
+    if cmd_opts.device_allocator_heap_key:
+        vulkan_runtime_flags += [
+            f"--device_allocator=caching:device_local={cmd_opts.device_allocator_heap_key}",
+        ]
+    set_iree_vulkan_runtime_flags(flags=vulkan_runtime_flags)
+
+
+def parse_device(device_str, target_override=""):
+    from shark.iree_utils.compile_utils import (
+        clean_device_info,
+        get_iree_target_triple,
+        iree_target_map,
+    )
+
+    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:
+        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":
+            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]
+    raise AssertionError(
+        f"Device {device_str} not recognized. Please file an issue at https://github.com/nod-ai/SHARK/issues."
+    )
+
+
+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 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
+
+
+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 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:
+            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 = []
+    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)
+    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)
+    return available_devices
+
+
+# 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"
+    )

apps/shark_studio/modules/ckpt_processing.py

@@ -0,0 +1,145 @@
+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])
+
+    irpa_file = weights_path.replace(".safetensors", ".irpa")
+    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 +1,202 @@
+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 = {}
+    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 +1,791 @@
+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 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))"
+    ],
+    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=50,
+    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=7.5,
+    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(
+    "--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/studio_imports.py

@@ -0,0 +1,68 @@
+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("omegaconf")
+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 += copy_metadata("scipy")
+datas += collect_data_files("torch")
+datas += collect_data_files("tokenizers")
+datas += collect_data_files("accelerate")
+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", include_py_files=True)
+datas += collect_data_files("tqdm")
+datas += collect_data_files("tkinter")
+datas += collect_data_files("sentencepiece")
+datas += collect_data_files("jsonschema")
+datas += collect_data_files("jsonschema_specifications")
+datas += collect_data_files("cpuinfo")
+datas += collect_data_files("scipy", include_py_files=True)
+datas += [
+    ("web/ui/css/*", "ui/css"),
+    ("web/ui/js/*", "ui/js"),
+    ("web/ui/logos/*", "logos"),
+]
+
+
+# hidden imports for pyinstaller
+hiddenimports = ["shark", "apps"]
+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"]
+hiddenimports += [x for x in collect_submodules("scipy") if "test" not in x]

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/web/api/compat.py

@@ -0,0 +1,286 @@
+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
+
+# from sdapi_v1 import shark_sd_api
+from apps.shark_studio.api.llm import llm_chat_api
+
+
+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.add_api_route("/sdapi/v1/img2img", shark_sd_api, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/upscaler", self.upscaler_api, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse)
+        # self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse)
+        # self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse)
+        # self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse)
+        # self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
+        # self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
+        # self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
+        # self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
+        # self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
+        # self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
+        # self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
+        # self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
+        # self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
+        # self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
+        # self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
+        # self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
+        # self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
+        # self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
+        # self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
+        # self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
+        # self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
+        # self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
+        # self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
+        # self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
+        # self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
+
+        # chat APIs needed for compatibility with multiple extensions using OpenAI API
+        self.add_api_route("/v1/chat/completions", llm_chat_api, methods=["POST"])
+        self.add_api_route("/v1/completions", llm_chat_api, methods=["POST"])
+        self.add_api_route("/chat/completions", llm_chat_api, methods=["POST"])
+        self.add_api_route("/completions", llm_chat_api, methods=["POST"])
+        self.add_api_route(
+            "/v1/engines/codegen/completions", llm_chat_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 refresh_checkpoints(self):
+    #     with self.queue_lock:
+    #         studio_data.refresh_checkpoints()
+
+    # def refresh_vae(self):
+    #     with self.queue_lock:
+    #         studio_data.refresh_vae_list()
+
+    # def unloadapi(self):
+    #     unload_model_weights()
+
+    #     return {}
+
+    # def reloadapi(self):
+    #     reload_model_weights()
+
+    #     return {}
+
+    # def skip(self):
+    #     studio.state.skip()
+
+    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 @@
+

apps/shark_studio/web/index.py

@@ -0,0 +1,222 @@
+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 fastapi import FastAPI
+    from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
+
+    initialize.initialize()
+
+    app = FastAPI()
+    initialize.setup_middleware(app)
+    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
+
+    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 Beta",
+    ) 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
+
+    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

@@ -105,7 +105,19 @@ body {
     background-color: var(--background-fill-primary);
 }
 
-/* display in full width for desktop devices */
+.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 {
@@ -113,25 +125,30 @@ body {
     }
 }
 
+/* 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;
 }
 
-.container {
-    background-color: black !important;
-    padding-top: var(--size-5) !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);
 }
 
-#top_logo {
-    background-color: transparent;
-    border-radius: 0 !important;
-    border: 0;
-}
-
 #demo_title_outer {
     border-radius: 0;
 }
@@ -174,6 +191,7 @@ footer {
     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{
@@ -185,6 +203,20 @@ footer {
         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;
@@ -226,18 +258,61 @@ footer {
     display:none;
 }
 
-/* Hide the download icon from the nod logo */
-#top_logo .download {
-    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 tbody td {
-    font-size: small;
-    line-height: var(--line-xs)
+.output_parameters_dataframe table.table {
+    /* works around a gradio bug that always shows scrollbars */
+    overflow: clip auto;
 }
 
-#output_refresh_button {
+.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;
@@ -252,6 +327,15 @@ footer {
     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;
@@ -263,7 +347,7 @@ footer {
     width: 100%;
 }
 
-#top_logo.logo_centered img{
+#top_logo.logo_centered img {
     object-fit: scale-down;
     position: absolute;
     width: 80%;
@@ -271,3 +355,19 @@ footer {
     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

@@ -1,15 +1,17 @@
 import glob
 import gradio as gr
 import os
+import subprocess
+import sys
 from PIL import Image
 
-from apps.stable_diffusion.src import args
-from apps.stable_diffusion.src.utils import (
+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.stable_diffusion.web.ui.utils import nodlogo_loc
-from apps.stable_diffusion.web.utils.metadata import displayable_metadata
+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
 
@@ -20,8 +22,7 @@ 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")
+            glob.glob(new_dir_path + "/" + ext) for ext in ("*.png", "*.jpg", "*.jpeg")
         ]
 
         return sorted(sum(filenames, []), key=os.path.getmtime, reverse=True)
@@ -34,27 +35,23 @@ def output_subdirs() -> list[str]:
     relative_paths = [
         os.path.relpath(entry[0], output_dir)
         for entry in os.walk(
-            output_dir, followlinks=args.output_gallery_followlinks
+            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
+    # 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 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.
+    # 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 != "."
-        ]
+        [path for path in relative_paths if (not path.isnumeric()) and path != "."]
     )
 
     return result_paths
@@ -62,11 +59,12 @@ def output_subdirs() -> list[str]:
 
 # --- Define UI layout for Gradio
 
-with gr.Blocks() as outputgallery_web:
-    nod_logo = Image.open(nodlogo_loc)
+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
+        # needed to workaround gradio issue:
+        # https://github.com/gradio-app/gradio/issues/2907
         dev_null = gr.Textbox("", visible=False)
 
         gallery_files = gr.State(value=[])
@@ -75,12 +73,13 @@ with gr.Blocks() as outputgallery_web:
         with gr.Column(scale=6):
             logo = gr.Image(
                 label="Getting subdirectories...",
-                value=nod_logo,
+                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(
@@ -88,33 +87,57 @@ with gr.Blocks() as outputgallery_web:
                 value=gallery_files.value,
                 visible=False,
                 show_label=True,
-            ).style(columns=4)
+                columns=4,
+            )
 
         with gr.Column(scale=4):
-            with gr.Box():
+            with gr.Group():
                 with gr.Row():
-                    with gr.Column(scale=16, min_width=160):
+                    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,
-                        ).style(container=False)
+                            elem_classes="dropdown_no_container",
+                            allow_custom_value=True,
+                        )
                     with gr.Column(
-                        scale=1, min_width=32, elem_id="output_refresh_button"
+                        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
-                        ).style(size="sm")
+                            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
-            ).style(show_copy_button=True)
+                label="Filename",
+                value="None",
+                interactive=False,
+                show_copy_button=True,
+            )
 
             with gr.Accordion(
                 label="Parameter Information", open=False
@@ -125,139 +148,143 @@ with gr.Blocks() as outputgallery_web:
                     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_txt2img = gr.Button(
-                        value="Txt2Img",
+                    outputgallery_sendto_sd = gr.Button(
+                        value="Stable Diffusion",
                         interactive=False,
                         elem_classes="outputgallery_sendto",
-                    ).style(size="sm")
-
-                    outputgallery_sendto_img2img = gr.Button(
-                        value="Img2Img",
-                        interactive=False,
-                        elem_classes="outputgallery_sendto",
-                    ).style(size="sm")
-
-                    outputgallery_sendto_inpaint = gr.Button(
-                        value="Inpaint",
-                        interactive=False,
-                        elem_classes="outputgallery_sendto",
-                    ).style(size="sm")
-
-                    outputgallery_sendto_outpaint = gr.Button(
-                        value="Outpaint",
-                        interactive=False,
-                        elem_classes="outputgallery_sendto",
-                    ).style(size="sm")
-
-                    outputgallery_sendto_upscaler = gr.Button(
-                        value="Upscaler",
-                        interactive=False,
-                        elem_classes="outputgallery_sendto",
-                    ).style(size="sm")
+                        size="sm",
+                    )
 
     # --- Event handlers
 
     def on_clear_gallery():
         return [
-            gr.Gallery.update(
+            gr.Gallery(
                 value=[],
                 visible=False,
             ),
-            gr.Image.update(
+            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)}"
-        )
+        new_label = f"{len(new_images)} images in {os.path.join(output_dir, subdir)}"
         return [
             new_images,
-            gr.Gallery.update(
+            gr.Gallery(
                 value=new_images,
                 label=new_label,
                 visible=len(new_images) > 0,
             ),
-            gr.Image.update(
+            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
+        # 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
+        # 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 {os.path.join(output_dir, new_subdir)}"
+        new_label = (
+            f"{len(new_images)} images in " f"{os.path.join(output_dir, new_subdir)}"
+        )
 
         return [
-            gr.Dropdown.update(
+            gr.Dropdown(
                 choices=refreshed_subdirs,
                 value=new_subdir,
             ),
             refreshed_subdirs,
             new_images,
-            gr.Gallery.update(
-                value=new_images, label=new_label, visible=len(new_images) > 0
-            ),
-            gr.Image.update(
+            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
+        # 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
+        # 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 {os.path.join(output_dir, subdir)} - {status}"
+            new_label = (
+                f"{len(new_images)} images in "
+                f"{os.path.join(output_dir, subdir)} - {status}"
+            )
 
             return [
                 new_images,
-                gr.Gallery.update(
+                gr.Gallery(
                     value=new_images,
                     label=new_label,
                     visible=len(new_images) > 0,
                 ),
-                gr.Image.update(
+                gr.Image(
                     label=new_label,
                     visible=len(new_images) == 0,
                 ),
             ]
         else:
-            # otherwise change nothing, (only untyped gradio gr.update() does this)
+            # 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
+        # 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:
-            return [
-                filename,
-                list(map(list, params["parameters"].items())),
-            ]
+            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,
@@ -267,26 +294,28 @@ with gr.Blocks() as outputgallery_web:
     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.update(interactive=exists),
-            gr.Button.update(interactive=exists),
-            gr.Button.update(interactive=exists),
-            gr.Button.update(interactive=exists),
-            gr.Button.update(interactive=exists),
-            gr.Button.update(interactive=exists),
+            # 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.
+    # 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):
+    # 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("")
+            return on_refresh("") + [gr.update(interactive=local_client)]
         else:
             return (
                 # Change nothing, (only untyped gr.update() does this)
@@ -295,55 +324,13 @@ with gr.Blocks() as outputgallery_web:
                 gr.update(),
                 gr.update(),
                 gr.update(),
+                gr.update(),
             )
 
-    # Unfortunately as of gradio 3.22.0 gr.update against Galleries doesn't support
-    # things set with .style, nor the elem_classes kwarg so we have to directly set
-    # things up via JavaScript if we want the client to take notice of any of our
-    # changes to the number of columns after it decides to put them back to the
-    # original number when we change something
-    def js_set_columns_in_browser(timeout_length):
-        return f"""
-            (new_cols) => {{
-                setTimeout(() => {{
-                    required_style = "auto ".repeat(new_cols).trim();
-                    gallery = document.querySelector('#outputgallery_gallery .grid-container');
-                    if (gallery) {{
-                        gallery.style.gridTemplateColumns = required_style
-                    }}
-                }}, {timeout_length});
-                return [];      // prevents console error from gradio
-            }}
-        """
-
-    # --- Wire handlers up to the actions
-
-    # - Many actions reset the number of columns shown in the gallery on the browser end,
-    #   so we have to set them back to what we think they should be after the initial
-    #   action.
-    # - None of the actions on this tab trigger inference, and we want the user to be able
-    #   to do them whilst other tabs have ongoing inference running. Waiting in the queue
-    #   behind inference jobs would mean the UI can't fully respond until the inference tasks
-    #   complete, hence queue=False on all of these.
-    set_gallery_columns_immediate = dict(
-        fn=None,
-        inputs=[image_columns],
-        # gradio blanks the UI on Chrome on Linux on gallery select if I don't put an output here
-        outputs=[dev_null],
-        _js=js_set_columns_in_browser(0),
-        queue=False,
-    )
-
-    # setting columns after selecting a gallery item needs a real timeout length for the
-    # number of columns to actually be applied. Not really sure why, maybe something has
-    # to finish animating?
-    set_gallery_columns_delayed = dict(
-        set_gallery_columns_immediate, _js=js_set_columns_in_browser(250)
-    )
-
-    # 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.
+    # 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,
@@ -351,38 +338,41 @@ with gr.Blocks() as outputgallery_web:
         queue=False,
     )
 
-    image_columns.change(**set_gallery_columns_immediate)
-
     subdirectories.select(**clear_gallery).then(
         on_select_subdir,
         [subdirectories],
         [gallery_files, gallery, logo],
         queue=False,
-    ).then(**set_gallery_columns_immediate)
+    )
+
+    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,
-    ).then(**set_gallery_columns_immediate)
+    )
+
+    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,
-    ).then(**set_gallery_columns_delayed)
+    )
 
     outputgallery_filename.change(
         on_outputgallery_filename_change,
         [outputgallery_filename],
         [
-            outputgallery_sendto_txt2img,
-            outputgallery_sendto_img2img,
-            outputgallery_sendto_inpaint,
-            outputgallery_sendto_outpaint,
-            outputgallery_sendto_upscaler,
+            outputgallery_sendto_sd,
         ],
         queue=False,
     )
@@ -398,9 +388,10 @@ with gr.Blocks() as outputgallery_web:
                 gallery_files,
                 gallery,
                 logo,
+                open_subdir,
             ],
             queue=False,
-        ).then(**set_gallery_columns_immediate)
+        )
 
     # 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
@@ -412,4 +403,4 @@ with gr.Blocks() as outputgallery_web:
                 inputs=[subdirectories, subdirectory_paths, component],
                 outputs=[gallery_files, gallery, logo],
                 queue=False,
-            ).then(**set_gallery_columns_immediate)
+            )

apps/shark_studio/web/ui/sd.py

@@ -0,0 +1,777 @@
+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,
+    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
+
+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",
+]
+
+
+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):
+    new_sd_config = none_to_str_none(json.loads(view_json_file(load_sd_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
+
+    return [
+        sd_json["prompt"][0],
+        sd_json["negative_prompt"][0],
+        sd_image,
+        sd_json["height"],
+        sd_json["width"],
+        sd_json["steps"],
+        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 "..."
+
+
+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")
+
+    return gr.Dropdown(
+        value=new_choices[0] if len(new_choices) > 0 else "None",
+        choices=["None"] + new_choices,
+    )
+
+
+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.Accordion(
+                    label="\U0001F4D0\U0000FE0F Device Settings", open=False
+                ):
+                    device = gr.Dropdown(
+                        elem_id="device",
+                        label="Device",
+                        value=global_obj.get_device_list()[0],
+                        choices=global_obj.get_device_list(),
+                        allow_custom_value=False,
+                    )
+                    target_triple = gr.Textbox(
+                        elem_id="target_triple",
+                        label="Architecture",
+                        value="",
+                    )
+                    with gr.Row():
+                        ondemand = gr.Checkbox(
+                            value=cmd_opts.lowvram,
+                            label="Low VRAM",
+                            interactive=True,
+                        )
+                        precision = gr.Radio(
+                            label="Precision",
+                            value=cmd_opts.precision,
+                            choices=[
+                                "fp16",
+                                "fp32",
+                            ],
+                            visible=True,
+                        )
+                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="stabilityai/stable-diffusion-2-1-base",
+                    choices=sd_default_models,
+                    allow_custom_value=True,
+                )  # base_model_id
+                with gr.Row():
+                    height = gr.Slider(
+                        384,
+                        1024,
+                        value=cmd_opts.height,
+                        step=8,
+                        label="\U00002195\U0000FE0F Height",
+                    )
+                    width = gr.Slider(
+                        384,
+                        1024,
+                        value=cmd_opts.width,
+                        step=8,
+                        label="\U00002194\U0000FE0F Width",
+                    )
+                with gr.Accordion(
+                    label="\U00002696\U0000FE0F Model Weights", open=False
+                ):
+                    with gr.Column():
+                        custom_weights = gr.Dropdown(
+                            label="Checkpoint Weights",
+                            info="Select or enter HF model ID",
+                            elem_id="custom_model",
+                            value="None",
+                            allow_custom_value=True,
+                            choices=["None"]
+                            + get_checkpoints(os.path.basename(str(base_model_id))),
+                        )  # custom_weights
+                        base_model_id.change(
+                            fn=base_model_changed,
+                            inputs=[base_model_id],
+                            outputs=[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=(
+                                os.path.basename(cmd_opts.custom_vae)
+                                if cmd_opts.custom_vae
+                                else "None"
+                            ),
+                            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=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="\U0001F9EA\U0000FE0F Input Image Processing", open=False
+                ):
+                    strength = gr.Slider(
+                        0,
+                        1,
+                        value=cmd_opts.strength,
+                        step=0.01,
+                        label="Denoising Strength",
+                    )
+                    resample_type = gr.Dropdown(
+                        value=cmd_opts.resample_type,
+                        choices=resampler_list,
+                        label="Resample Type",
+                        allow_custom_value=True,
+                    )
+                with gr.Group(elem_id="prompt_box_outer"):
+                    prompt = gr.Textbox(
+                        label="\U00002795\U0000FE0F Prompt",
+                        value=cmd_opts.prompt[0],
+                        lines=2,
+                        elem_id="prompt_box",
+                        show_copy_button=True,
+                    )
+                    negative_prompt = gr.Textbox(
+                        label="\U00002796\U0000FE0F Negative Prompt",
+                        value=cmd_opts.negative_prompt[0],
+                        lines=2,
+                        elem_id="negative_prompt_box",
+                        show_copy_button=True,
+                    )
+                with gr.Row(equal_height=True):
+                    seed = gr.Textbox(
+                        value=cmd_opts.seed,
+                        label="\U0001F331\U0000FE0F Seed",
+                        info="An integer or a JSON list of integers, -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="EulerDiscrete",
+                        choices=scheduler_model_map.keys(),
+                        allow_custom_value=False,
+                    )
+                with gr.Row():
+                    steps = gr.Slider(
+                        1,
+                        100,
+                        value=cmd_opts.steps,
+                        step=1,
+                        label="\U0001F3C3\U0000FE0F Steps",
+                    )
+                    guidance_scale = gr.Slider(
+                        0,
+                        50,
+                        value=cmd_opts.guidance_scale,
+                        step=0.1,
+                        label="\U0001F5C3\U0000FE0F CFG Scale",
+                    )
+                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=256,
+                                    maximum=1024,
+                                    value=512,
+                                    step=8,
+                                )
+                                canvas_height = gr.Slider(
+                                    label="Canvas Height",
+                                    minimum=256,
+                                    maximum=1024,
+                                    value=512,
+                                    step=8,
+                                )
+                            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) as sd_tab_init_image:
+                        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():
+                                batch_count = gr.Slider(
+                                    1,
+                                    100,
+                                    value=cmd_opts.batch_count,
+                                    step=1,
+                                    label="Batch Count",
+                                    interactive=True,
+                                )
+                                batch_size = gr.Slider(
+                                    1,
+                                    4,
+                                    value=cmd_opts.batch_size,
+                                    step=1,
+                                    label="Batch Size",
+                                    interactive=True,
+                                    visible=True,
+                                )
+                                compiled_pipeline = gr.Checkbox(
+                                    False,
+                                    label="Faster txt2img (SDXL only)",
+                                )
+                            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")
+                    with gr.Tab(label="Config", id=102) as sd_tab_config:
+                        with gr.Column(elem_classes=["sd-right-panel"]):
+                            with gr.Row(elem_classes=["fill"]):
+                                Path(get_configs_path()).mkdir(
+                                    parents=True, exist_ok=True
+                                )
+                                default_config_file = os.path.join(
+                                    get_configs_path(),
+                                    "default_sd_config.json",
+                                )
+                                write_default_sd_configs(get_configs_path())
+                                sd_json = gr.JSON(
+                                    elem_classes=["fill"],
+                                    value=view_json_file(default_config_file),
+                                )
+                            with gr.Row():
+                                with gr.Column(scale=3):
+                                    load_sd_config = gr.FileExplorer(
+                                        label="Load Config",
+                                        file_count="single",
+                                        root_dir=(
+                                            cmd_opts.configs_path
+                                            if cmd_opts.configs_path
+                                            else get_configs_path()
+                                        ),
+                                        height=75,
+                                    )
+                                with gr.Column(scale=1):
+                                    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,
+                                )
+                                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],
+                                )
+                        save_sd_config.click(
+                            fn=save_sd_cfg,
+                            inputs=[sd_json, sd_config_name],
+                            outputs=[sd_config_name],
+                        )
+                    with gr.Tab(label="Log", id=103) 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)
+
+    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": default_sd_config,
+    "sdxl-30steps.json": sdxl_30steps,
+    "sdxl-turbo.json": sdxl_turbo,
+}

apps/shark_studio/web/utils/file_utils.py

@@ -0,0 +1,102 @@
+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)
+        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 == "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_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 +1,134 @@
+import gc
+from ...api.utils import get_available_devices
+
+"""
+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 _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_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/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

@@ -8,6 +8,9 @@ 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,

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

@@ -50,7 +50,22 @@ PARAMS_FORMATS = {
     },
 }
 
-PARAMS_FORMAT_LONGEST = PARAMS_FORMATS[max(PARAMS_FORMATS.keys())]
+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:
@@ -76,6 +91,14 @@ def compact(metadata: dict) -> dict:
         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
 
 
@@ -97,19 +120,20 @@ def humanize(metadata: dict | list[str], includes_filename=True) -> dict:
             )
 
     # For dictionaries we try to use the matching length parameter format if
-    # available, otherwise we use the longest. 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
+    # 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_LONGEST
+            format = PARAMS_FORMAT_CURRENT
 
         return {
-            format[key]: value
-            for (key, value) in metadata.items()
-            if key in format.keys()
+            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

@@ -2,14 +2,26 @@ import os
 import shutil
 from time import time
 
-shark_tmp = os.path.join(os.getcwd(), "shark_tmp/")
+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 config_gradio_tmp_imgs_folder():
-    # create shark_tmp if it does not exist
-    if not os.path.exists(shark_tmp):
-        os.mkdir(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:
@@ -52,3 +64,12 @@ def config_gradio_tmp_imgs_folder():
             )
         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=auto or "mhlo_legacy" or "stablehlo" 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.train_lora_word import lora_train

apps/stable_diffusion/scripts/img2img.py

@@ -1,126 +0,0 @@
-import sys
-import torch
-import time
-from PIL import Image
-import transformers
-from apps.stable_diffusion.src import (
-    args,
-    Image2ImagePipeline,
-    StencilPipeline,
-    resize_stencil,
-    get_schedulers,
-    set_init_device_flags,
-    utils,
-    clear_all,
-    save_output_img,
-)
-from apps.stable_diffusion.src.utils import get_generation_text_info
-
-
-def main():
-    if args.clear_all:
-        clear_all()
-
-    if args.img_path is None:
-        print("Flag --img_path is required.")
-        exit()
-
-    image = Image.open(args.img_path).convert("RGB")
-    # When the models get uploaded, it should be default to False.
-    args.import_mlir = True
-
-    use_stencil = args.use_stencil
-    if use_stencil:
-        args.scheduler = "DDIM"
-        args.hf_model_id = "runwayml/stable-diffusion-v1-5"
-        image, args.width, args.height = resize_stencil(image)
-    elif "Shark" in args.scheduler:
-        print(
-            f"Shark schedulers are not supported. Switching to EulerDiscrete scheduler"
-        )
-        args.scheduler = "EulerDiscrete"
-    cpu_scheduling = not args.scheduler.startswith("Shark")
-    dtype = torch.float32 if args.precision == "fp32" else torch.half
-    set_init_device_flags()
-    schedulers = get_schedulers(args.hf_model_id)
-    scheduler_obj = schedulers[args.scheduler]
-    seed = utils.sanitize_seed(args.seed)
-    # Adjust for height and width based on model
-
-    if use_stencil:
-        img2img_obj = StencilPipeline.from_pretrained(
-            scheduler_obj,
-            args.import_mlir,
-            args.hf_model_id,
-            args.ckpt_loc,
-            args.custom_vae,
-            args.precision,
-            args.max_length,
-            args.batch_size,
-            args.height,
-            args.width,
-            args.use_base_vae,
-            args.use_tuned,
-            low_cpu_mem_usage=args.low_cpu_mem_usage,
-            use_stencil=use_stencil,
-            debug=args.import_debug if args.import_mlir else False,
-            use_lora=args.use_lora,
-            ondemand=args.ondemand,
-        )
-    else:
-        img2img_obj = Image2ImagePipeline.from_pretrained(
-            scheduler_obj,
-            args.import_mlir,
-            args.hf_model_id,
-            args.ckpt_loc,
-            args.custom_vae,
-            args.precision,
-            args.max_length,
-            args.batch_size,
-            args.height,
-            args.width,
-            args.use_base_vae,
-            args.use_tuned,
-            low_cpu_mem_usage=args.low_cpu_mem_usage,
-            debug=args.import_debug if args.import_mlir else False,
-            use_lora=args.use_lora,
-            ondemand=args.ondemand,
-        )
-
-    start_time = time.time()
-    generated_imgs = img2img_obj.generate_images(
-        args.prompts,
-        args.negative_prompts,
-        image,
-        args.batch_size,
-        args.height,
-        args.width,
-        args.steps,
-        args.strength,
-        args.guidance_scale,
-        seed,
-        args.max_length,
-        dtype,
-        args.use_base_vae,
-        cpu_scheduling,
-        use_stencil=use_stencil,
-    )
-    total_time = time.time() - start_time
-    text_output = f"prompt={args.prompts}"
-    text_output += f"\nnegative prompt={args.negative_prompts}"
-    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
-    text_output += f"\nscheduler={args.scheduler}, device={args.device}"
-    text_output += f"\nsteps={args.steps}, strength={args.strength}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
-    text_output += (
-        f", batch size={args.batch_size}, max_length={args.max_length}"
-    )
-    text_output += img2img_obj.log
-    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
-
-    extra_info = {"STRENGTH": args.strength}
-    save_output_img(generated_imgs[0], seed, extra_info)
-    print(text_output)
-
-
-if __name__ == "__main__":
-    main()

apps/stable_diffusion/scripts/inpaint.py

@@ -1,104 +0,0 @@
-import torch
-import time
-from PIL import Image
-import transformers
-from apps.stable_diffusion.src import (
-    args,
-    InpaintPipeline,
-    get_schedulers,
-    set_init_device_flags,
-    utils,
-    clear_all,
-    save_output_img,
-)
-from apps.stable_diffusion.src.utils import get_generation_text_info
-
-
-def main():
-    if args.clear_all:
-        clear_all()
-
-    if args.img_path is None:
-        print("Flag --img_path is required.")
-        exit()
-    if args.mask_path is None:
-        print("Flag --mask_path is required.")
-        exit()
-
-    dtype = torch.float32 if args.precision == "fp32" else torch.half
-    cpu_scheduling = not args.scheduler.startswith("Shark")
-    set_init_device_flags()
-    model_id = (
-        args.hf_model_id
-        if "inpaint" in args.hf_model_id
-        else "stabilityai/stable-diffusion-2-inpainting"
-    )
-    schedulers = get_schedulers(model_id)
-    scheduler_obj = schedulers[args.scheduler]
-    seed = args.seed
-    image = Image.open(args.img_path)
-    mask_image = Image.open(args.mask_path)
-
-    inpaint_obj = InpaintPipeline.from_pretrained(
-        scheduler=scheduler_obj,
-        import_mlir=args.import_mlir,
-        model_id=args.hf_model_id,
-        ckpt_loc=args.ckpt_loc,
-        custom_vae=args.custom_vae,
-        precision=args.precision,
-        max_length=args.max_length,
-        batch_size=args.batch_size,
-        height=args.height,
-        width=args.width,
-        use_base_vae=args.use_base_vae,
-        use_tuned=args.use_tuned,
-        low_cpu_mem_usage=args.low_cpu_mem_usage,
-        debug=args.import_debug if args.import_mlir else False,
-        use_lora=args.use_lora,
-        ondemand=args.ondemand,
-    )
-
-    for current_batch in range(args.batch_count):
-        if current_batch > 0:
-            seed = -1
-        seed = utils.sanitize_seed(seed)
-
-        start_time = time.time()
-        generated_imgs = inpaint_obj.generate_images(
-            args.prompts,
-            args.negative_prompts,
-            image,
-            mask_image,
-            args.batch_size,
-            args.height,
-            args.width,
-            args.inpaint_full_res,
-            args.inpaint_full_res_padding,
-            args.steps,
-            args.guidance_scale,
-            seed,
-            args.max_length,
-            dtype,
-            args.use_base_vae,
-            cpu_scheduling,
-        )
-        total_time = time.time() - start_time
-        text_output = f"prompt={args.prompts}"
-        text_output += f"\nnegative prompt={args.negative_prompts}"
-        text_output += (
-            f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
-        )
-        text_output += f"\nscheduler={args.scheduler}, device={args.device}"
-        text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
-        text_output += (
-            f", batch size={args.batch_size}, max_length={args.max_length}"
-        )
-        text_output += inpaint_obj.log
-        text_output += f"\nTotal image generation time: {total_time:.4f}sec"
-
-        save_output_img(generated_imgs[0], seed)
-        print(text_output)
-
-
-if __name__ == "__main__":
-    main()

apps/stable_diffusion/scripts/main.py

@@ -1,19 +0,0 @@
-from apps.stable_diffusion.src import args
-from apps.stable_diffusion.scripts import (
-    img2img,
-    txt2img,
-    #    inpaint,
-    #    outpaint,
-)
-
-if __name__ == "__main__":
-    if args.app == "txt2img":
-        txt2img.main()
-    elif args.app == "img2img":
-        img2img.main()
-    #   elif args.app == "inpaint":
-    #       inpaint.main()
-    #   elif args.app == "outpaint":
-    #       outpaint.main()
-    else:
-        print(f"args.app value is {args.app} but this isn't supported")

apps/stable_diffusion/scripts/outpaint.py

@@ -1,119 +0,0 @@
-import torch
-import time
-from PIL import Image
-import transformers
-from apps.stable_diffusion.src import (
-    args,
-    OutpaintPipeline,
-    get_schedulers,
-    set_init_device_flags,
-    utils,
-    clear_all,
-    save_output_img,
-)
-
-
-def main():
-    if args.clear_all:
-        clear_all()
-
-    if args.img_path is None:
-        print("Flag --img_path is required.")
-        exit()
-
-    dtype = torch.float32 if args.precision == "fp32" else torch.half
-    cpu_scheduling = not args.scheduler.startswith("Shark")
-    set_init_device_flags()
-    model_id = (
-        args.hf_model_id
-        if "inpaint" in args.hf_model_id
-        else "stabilityai/stable-diffusion-2-inpainting"
-    )
-    schedulers = get_schedulers(model_id)
-    scheduler_obj = schedulers[args.scheduler]
-    seed = args.seed
-    image = Image.open(args.img_path)
-
-    outpaint_obj = OutpaintPipeline.from_pretrained(
-        scheduler_obj,
-        args.import_mlir,
-        args.hf_model_id,
-        args.ckpt_loc,
-        args.custom_vae,
-        args.precision,
-        args.max_length,
-        args.batch_size,
-        args.height,
-        args.width,
-        args.use_base_vae,
-        args.use_tuned,
-        use_lora=args.use_lora,
-        ondemand=args.ondemand,
-    )
-
-    for current_batch in range(args.batch_count):
-        if current_batch > 0:
-            seed = -1
-        seed = utils.sanitize_seed(seed)
-
-        start_time = time.time()
-        generated_imgs = outpaint_obj.generate_images(
-            args.prompts,
-            args.negative_prompts,
-            image,
-            args.pixels,
-            args.mask_blur,
-            args.left,
-            args.right,
-            args.top,
-            args.bottom,
-            args.noise_q,
-            args.color_variation,
-            args.batch_size,
-            args.height,
-            args.width,
-            args.steps,
-            args.guidance_scale,
-            seed,
-            args.max_length,
-            dtype,
-            args.use_base_vae,
-            cpu_scheduling,
-        )
-        total_time = time.time() - start_time
-        text_output = f"prompt={args.prompts}"
-        text_output += f"\nnegative prompt={args.negative_prompts}"
-        text_output += (
-            f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
-        )
-        text_output += f"\nscheduler={args.scheduler}, device={args.device}"
-        text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
-        text_output += (
-            f", batch size={args.batch_size}, max_length={args.max_length}"
-        )
-        text_output += outpaint_obj.log
-        text_output += f"\nTotal image generation time: {total_time:.4f}sec"
-
-        # save this information as metadata of output generated image.
-        directions = []
-        if args.left:
-            directions.append("left")
-        if args.right:
-            directions.append("right")
-        if args.top:
-            directions.append("up")
-        if args.bottom:
-            directions.append("down")
-        extra_info = {
-            "PIXELS": args.pixels,
-            "MASK_BLUR": args.mask_blur,
-            "DIRECTIONS": directions,
-            "NOISE_Q": args.noise_q,
-            "COLOR_VARIATION": args.color_variation,
-        }
-        save_output_img(generated_imgs[0], seed, extra_info)
-        print(text_output)
-
-
-if __name__ == "__main__":
-    main()

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/train_lora_word.py

@@ -1,692 +0,0 @@
-# Install the required libs
-# pip install -U git+https://github.com/huggingface/diffusers.git
-# pip install accelerate transformers ftfy
-
-# HuggingFace Token
-# YOUR_TOKEN = "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"
-
-
-# Import required libraries
-import itertools
-import math
-import os
-from typing import List
-import random
-import torch_mlir
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-import PIL
-import logging
-
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    PNDMScheduler,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-)
-from PIL import Image
-from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-from diffusers.loaders import AttnProcsLayers
-from diffusers.models.cross_attention import LoRACrossAttnProcessor
-
-import torch_mlir
-from torch_mlir.dynamo import make_simple_dynamo_backend
-import torch._dynamo as dynamo
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch_mlir_e2e_test.linalg_on_tensors_backends import refbackend
-from shark.shark_inference import SharkInference
-
-torch._dynamo.config.verbose = True
-
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    PNDMScheduler,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.optimization import get_scheduler
-from diffusers.pipelines.stable_diffusion import (
-    StableDiffusionSafetyChecker,
-)
-from PIL import Image
-from tqdm.auto import tqdm
-from transformers import (
-    CLIPFeatureExtractor,
-    CLIPTextModel,
-    CLIPTokenizer,
-)
-
-from io import BytesIO
-
-from dataclasses import dataclass
-from apps.stable_diffusion.src import (
-    args,
-    get_schedulers,
-    set_init_device_flags,
-    clear_all,
-)
-from apps.stable_diffusion.src.utils import update_lora_weight
-
-
-# Setup the dataset
-class LoraDataset(Dataset):
-    def __init__(
-        self,
-        data_root,
-        tokenizer,
-        size=512,
-        repeats=100,
-        interpolation="bicubic",
-        set="train",
-        prompt="myloraprompt",
-        center_crop=False,
-    ):
-        self.data_root = data_root
-        self.tokenizer = tokenizer
-        self.size = size
-        self.center_crop = center_crop
-        self.prompt = prompt
-
-        self.image_paths = [
-            os.path.join(self.data_root, file_path)
-            for file_path in os.listdir(self.data_root)
-        ]
-
-        self.num_images = len(self.image_paths)
-        self._length = self.num_images
-
-        if set == "train":
-            self._length = self.num_images * repeats
-
-        self.interpolation = {
-            "linear": PIL.Image.LINEAR,
-            "bilinear": PIL.Image.BILINEAR,
-            "bicubic": PIL.Image.BICUBIC,
-            "lanczos": PIL.Image.LANCZOS,
-        }[interpolation]
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, i):
-        example = {}
-        image = Image.open(self.image_paths[i % self.num_images])
-
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-
-        example["input_ids"] = self.tokenizer(
-            self.prompt,
-            padding="max_length",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        ).input_ids[0]
-
-        # default to score-sde preprocessing
-        img = np.array(image).astype(np.uint8)
-
-        if self.center_crop:
-            crop = min(img.shape[0], img.shape[1])
-            (
-                h,
-                w,
-            ) = (
-                img.shape[0],
-                img.shape[1],
-            )
-            img = img[
-                (h - crop) // 2 : (h + crop) // 2,
-                (w - crop) // 2 : (w + crop) // 2,
-            ]
-
-        image = Image.fromarray(img)
-        image = image.resize(
-            (self.size, self.size), resample=self.interpolation
-        )
-
-        image = np.array(image).astype(np.uint8)
-        image = (image / 127.5 - 1.0).astype(np.float32)
-
-        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
-        return example
-
-
-def torch_device(device):
-    device_tokens = device.split("=>")
-    if len(device_tokens) == 1:
-        device_str = device_tokens[0].strip()
-    else:
-        device_str = device_tokens[1].strip()
-    device_type_tokens = device_str.split("://")
-    if device_type_tokens[0] == "metal":
-        device_type_tokens[0] = "vulkan"
-    if len(device_type_tokens) > 1:
-        return device_type_tokens[0] + ":" + device_type_tokens[1]
-    else:
-        return device_type_tokens[0]
-
-
-########## Setting up the model ##########
-def lora_train(
-    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,
-    training_images_dir: str,
-    lora_save_dir: str,
-    use_lora: str,
-):
-    from apps.stable_diffusion.web.ui.utils import (
-        get_custom_model_pathfile,
-        Config,
-    )
-    import apps.stable_diffusion.web.utils.global_obj as global_obj
-
-    print(
-        "Note LoRA training is not compatible with the latest torch-mlir branch"
-    )
-    print(
-        "To run LoRA training you'll need this to follow this guide for the torch-mlir branch: https://github.com/nod-ai/SHARK/tree/main/shark/examples/shark_training/stable_diffusion"
-    )
-    torch.manual_seed(seed)
-
-    args.prompts = [prompt]
-    args.steps = steps
-
-    # 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.training_images_dir = training_images_dir
-    args.lora_save_dir = lora_save_dir
-
-    args.precision = precision
-    args.batch_size = batch_size
-    args.max_length = max_length
-    args.height = height
-    args.width = width
-    args.device = torch_device(device)
-    args.use_lora = use_lora
-
-    # Load the Stable Diffusion model
-    text_encoder = CLIPTextModel.from_pretrained(
-        args.hf_model_id, subfolder="text_encoder"
-    )
-    vae = AutoencoderKL.from_pretrained(args.hf_model_id, subfolder="vae")
-    unet = UNet2DConditionModel.from_pretrained(
-        args.hf_model_id, subfolder="unet"
-    )
-
-    def freeze_params(params):
-        for param in params:
-            param.requires_grad = False
-
-    # Freeze everything but LoRA
-    freeze_params(vae.parameters())
-    freeze_params(unet.parameters())
-    freeze_params(text_encoder.parameters())
-
-    # Move vae and unet to device
-    vae.to(args.device)
-    unet.to(args.device)
-    text_encoder.to(args.device)
-
-    if use_lora != "":
-        update_lora_weight(unet, args.use_lora, "unet")
-    else:
-        lora_attn_procs = {}
-        for name in unet.attn_processors.keys():
-            cross_attention_dim = (
-                None
-                if name.endswith("attn1.processor")
-                else unet.config.cross_attention_dim
-            )
-            if name.startswith("mid_block"):
-                hidden_size = unet.config.block_out_channels[-1]
-            elif name.startswith("up_blocks"):
-                block_id = int(name[len("up_blocks.")])
-                hidden_size = list(reversed(unet.config.block_out_channels))[
-                    block_id
-                ]
-            elif name.startswith("down_blocks"):
-                block_id = int(name[len("down_blocks.")])
-                hidden_size = unet.config.block_out_channels[block_id]
-
-            lora_attn_procs[name] = LoRACrossAttnProcessor(
-                hidden_size=hidden_size,
-                cross_attention_dim=cross_attention_dim,
-            )
-
-        unet.set_attn_processor(lora_attn_procs)
-    lora_layers = AttnProcsLayers(unet.attn_processors)
-
-    class VaeModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.vae = vae
-
-        def forward(self, input):
-            x = self.vae.encode(input, return_dict=False)[0]
-            return x
-
-    class UnetModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.unet = unet
-
-        def forward(self, x, y, z):
-            return self.unet.forward(x, y, z, return_dict=False)[0]
-
-    shark_vae = VaeModel()
-    shark_unet = UnetModel()
-
-    ####### Creating our training data ########
-
-    tokenizer = CLIPTokenizer.from_pretrained(
-        args.hf_model_id,
-        subfolder="tokenizer",
-    )
-
-    # Let's create the Dataset and Dataloader
-    train_dataset = LoraDataset(
-        data_root=args.training_images_dir,
-        tokenizer=tokenizer,
-        size=vae.sample_size,
-        prompt=args.prompts[0],
-        repeats=100,
-        center_crop=False,
-        set="train",
-    )
-
-    def create_dataloader(train_batch_size=1):
-        return torch.utils.data.DataLoader(
-            train_dataset, batch_size=train_batch_size, shuffle=True
-        )
-
-    # Create noise_scheduler for training
-    noise_scheduler = DDPMScheduler.from_config(
-        args.hf_model_id, subfolder="scheduler"
-    )
-
-    ######## Training ###########
-
-    # Define hyperparameters for our training. If you are not happy with your results,
-    # you can tune the `learning_rate` and the `max_train_steps`
-
-    # Setting up all training args
-    hyperparameters = {
-        "learning_rate": 5e-04,
-        "scale_lr": True,
-        "max_train_steps": steps,
-        "train_batch_size": batch_size,
-        "gradient_accumulation_steps": 1,
-        "gradient_checkpointing": True,
-        "mixed_precision": "fp16",
-        "seed": 42,
-        "output_dir": "sd-concept-output",
-    }
-    # creating output directory
-    cwd = os.getcwd()
-    out_dir = os.path.join(cwd, hyperparameters["output_dir"])
-    while not os.path.exists(str(out_dir)):
-        try:
-            os.mkdir(out_dir)
-        except OSError as error:
-            print("Output directory not created")
-
-    ###### Torch-MLIR Compilation ######
-
-    def _remove_nones(fx_g: torch.fx.GraphModule) -> List[int]:
-        removed_indexes = []
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, (list, tuple)):
-                    node_arg = list(node_arg)
-                    node_args_len = len(node_arg)
-                    for i in range(node_args_len):
-                        curr_index = node_args_len - (i + 1)
-                        if node_arg[curr_index] is None:
-                            removed_indexes.append(curr_index)
-                            node_arg.pop(curr_index)
-                    node.args = (tuple(node_arg),)
-                    break
-
-        if len(removed_indexes) > 0:
-            fx_g.graph.lint()
-            fx_g.graph.eliminate_dead_code()
-            fx_g.recompile()
-        removed_indexes.sort()
-        return removed_indexes
-
-    def _unwrap_single_tuple_return(fx_g: torch.fx.GraphModule) -> bool:
-        """
-        Replace tuple with tuple element in functions that return one-element tuples.
-        Returns true if an unwrapping took place, and false otherwise.
-        """
-        unwrapped_tuple = False
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, tuple):
-                    if len(node_arg) == 1:
-                        node.args = (node_arg[0],)
-                        unwrapped_tuple = True
-                        break
-
-        if unwrapped_tuple:
-            fx_g.graph.lint()
-            fx_g.recompile()
-        return unwrapped_tuple
-
-    def _returns_nothing(fx_g: torch.fx.GraphModule) -> bool:
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, tuple):
-                    return len(node_arg) == 0
-        return False
-
-    def transform_fx(fx_g):
-        for node in fx_g.graph.nodes:
-            if node.op == "call_function":
-                if node.target in [
-                    torch.ops.aten.empty,
-                ]:
-                    # aten.empty should be filled with zeros.
-                    if node.target in [torch.ops.aten.empty]:
-                        with fx_g.graph.inserting_after(node):
-                            new_node = fx_g.graph.call_function(
-                                torch.ops.aten.zero_,
-                                args=(node,),
-                            )
-                            node.append(new_node)
-                            node.replace_all_uses_with(new_node)
-                            new_node.args = (node,)
-
-        fx_g.graph.lint()
-
-    @make_simple_dynamo_backend
-    def refbackend_torchdynamo_backend(
-        fx_graph: torch.fx.GraphModule, example_inputs: List[torch.Tensor]
-    ):
-        # handling usage of empty tensor without initializing
-        transform_fx(fx_graph)
-        fx_graph.recompile()
-        if _returns_nothing(fx_graph):
-            return fx_graph
-        removed_none_indexes = _remove_nones(fx_graph)
-        was_unwrapped = _unwrap_single_tuple_return(fx_graph)
-
-        mlir_module = torch_mlir.compile(
-            fx_graph, example_inputs, output_type="linalg-on-tensors"
-        )
-
-        bytecode_stream = BytesIO()
-        mlir_module.operation.write_bytecode(bytecode_stream)
-        bytecode = bytecode_stream.getvalue()
-
-        shark_module = SharkInference(
-            mlir_module=bytecode, device=args.device, mlir_dialect="tm_tensor"
-        )
-        shark_module.compile()
-
-        def compiled_callable(*inputs):
-            inputs = [x.numpy() for x in inputs]
-            result = shark_module("forward", inputs)
-            if was_unwrapped:
-                result = [
-                    result,
-                ]
-            if not isinstance(result, list):
-                result = torch.from_numpy(result)
-            else:
-                result = tuple(torch.from_numpy(x) for x in result)
-                result = list(result)
-                for removed_index in removed_none_indexes:
-                    result.insert(removed_index, None)
-                result = tuple(result)
-            return result
-
-        return compiled_callable
-
-    def predictions(torch_func, jit_func, batchA, batchB):
-        res = jit_func(batchA.numpy(), batchB.numpy())
-        if res is not None:
-            # prediction = torch.from_numpy(res)
-            prediction = res
-        else:
-            prediction = None
-        return prediction
-
-    logger = logging.getLogger(__name__)
-
-    train_batch_size = hyperparameters["train_batch_size"]
-    gradient_accumulation_steps = hyperparameters[
-        "gradient_accumulation_steps"
-    ]
-    learning_rate = hyperparameters["learning_rate"]
-    if hyperparameters["scale_lr"]:
-        learning_rate = (
-            learning_rate
-            * gradient_accumulation_steps
-            * train_batch_size
-            # * accelerator.num_processes
-        )
-
-    # Initialize the optimizer
-    optimizer = torch.optim.AdamW(
-        lora_layers.parameters(),  # only optimize the embeddings
-        lr=learning_rate,
-    )
-
-    # Training function
-    def train_func(batch_pixel_values, batch_input_ids):
-        # Convert images to latent space
-        latents = shark_vae(batch_pixel_values).sample().detach()
-        latents = latents * 0.18215
-
-        # Sample noise that we'll add to the latents
-        noise = torch.randn_like(latents)
-        bsz = latents.shape[0]
-        # Sample a random timestep for each image
-        timesteps = torch.randint(
-            0,
-            noise_scheduler.num_train_timesteps,
-            (bsz,),
-            device=latents.device,
-        ).long()
-
-        # Add noise to the latents according to the noise magnitude at each timestep
-        # (this is the forward diffusion process)
-        noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-        # Get the text embedding for conditioning
-        encoder_hidden_states = text_encoder(batch_input_ids)[0]
-
-        # Predict the noise residual
-        noise_pred = shark_unet(
-            noisy_latents,
-            timesteps,
-            encoder_hidden_states,
-        )
-
-        # Get the target for loss depending on the prediction type
-        if noise_scheduler.config.prediction_type == "epsilon":
-            target = noise
-        elif noise_scheduler.config.prediction_type == "v_prediction":
-            target = noise_scheduler.get_velocity(latents, noise, timesteps)
-        else:
-            raise ValueError(
-                f"Unknown prediction type {noise_scheduler.config.prediction_type}"
-            )
-
-        loss = (
-            F.mse_loss(noise_pred, target, reduction="none")
-            .mean([1, 2, 3])
-            .mean()
-        )
-        loss.backward()
-
-        optimizer.step()
-        optimizer.zero_grad()
-
-        return loss
-
-    def training_function():
-        max_train_steps = hyperparameters["max_train_steps"]
-        output_dir = hyperparameters["output_dir"]
-        gradient_checkpointing = hyperparameters["gradient_checkpointing"]
-
-        train_dataloader = create_dataloader(train_batch_size)
-
-        # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-        num_update_steps_per_epoch = math.ceil(
-            len(train_dataloader) / gradient_accumulation_steps
-        )
-        num_train_epochs = math.ceil(
-            max_train_steps / num_update_steps_per_epoch
-        )
-
-        # Train!
-        total_batch_size = (
-            train_batch_size
-            * gradient_accumulation_steps
-            # train_batch_size * accelerator.num_processes * gradient_accumulation_steps
-        )
-
-        logger.info("***** Running training *****")
-        logger.info(f"  Num examples = {len(train_dataset)}")
-        logger.info(
-            f"  Instantaneous batch size per device = {train_batch_size}"
-        )
-        logger.info(
-            f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
-        )
-        logger.info(
-            f"  Gradient Accumulation steps = {gradient_accumulation_steps}"
-        )
-        logger.info(f"  Total optimization steps = {max_train_steps}")
-        # Only show the progress bar once on each machine.
-        progress_bar = tqdm(
-            # range(max_train_steps), disable=not accelerator.is_local_main_process
-            range(max_train_steps)
-        )
-        progress_bar.set_description("Steps")
-        global_step = 0
-
-        params__ = [
-            i for i in text_encoder.get_input_embeddings().parameters()
-        ]
-
-        for epoch in range(num_train_epochs):
-            unet.train()
-            for step, batch in enumerate(train_dataloader):
-                dynamo_callable = dynamo.optimize(
-                    refbackend_torchdynamo_backend
-                )(train_func)
-                lam_func = lambda x, y: dynamo_callable(
-                    torch.from_numpy(x), torch.from_numpy(y)
-                )
-                loss = predictions(
-                    train_func,
-                    lam_func,
-                    batch["pixel_values"],
-                    batch["input_ids"],
-                )
-
-                # Checks if the accelerator has performed an optimization step behind the scenes
-                progress_bar.update(1)
-                global_step += 1
-
-                logs = {"loss": loss.detach().item()}
-                progress_bar.set_postfix(**logs)
-
-                if global_step >= max_train_steps:
-                    break
-
-    training_function()
-
-    # Save the lora weights
-    unet.save_attn_procs(args.lora_save_dir)
-
-    for param in itertools.chain(unet.parameters(), text_encoder.parameters()):
-        if param.grad is not None:
-            del param.grad  # free some memory
-        torch.cuda.empty_cache()
-
-
-if __name__ == "__main__":
-    if args.clear_all:
-        clear_all()
-
-    dtype = torch.float32 if args.precision == "fp32" else torch.half
-    cpu_scheduling = not args.scheduler.startswith("Shark")
-    set_init_device_flags()
-    schedulers = get_schedulers(args.hf_model_id)
-    scheduler_obj = schedulers[args.scheduler]
-    seed = args.seed
-    if len(args.prompts) != 1:
-        print("Need exactly one prompt for the LoRA word")
-    lora_train(
-        args.prompts[0],
-        args.height,
-        args.width,
-        args.training_steps,
-        args.guidance_scale,
-        args.seed,
-        args.batch_count,
-        args.batch_size,
-        args.scheduler,
-        "None",
-        args.hf_model_id,
-        args.precision,
-        args.device,
-        args.max_length,
-        args.training_images_dir,
-        args.lora_save_dir,
-        args.use_lora,
-    )

apps/stable_diffusion/scripts/tuner.py

@@ -1,126 +0,0 @@
-import os
-from pathlib import Path
-from shark_tuner.codegen_tuner import SharkCodegenTuner
-from shark_tuner.iree_utils import (
-    dump_dispatches,
-    create_context,
-    export_module_to_mlir_file,
-)
-from shark_tuner.model_annotation import model_annotation
-from apps.stable_diffusion.src.utils.stable_args import args
-from apps.stable_diffusion.src.utils.utils import set_init_device_flags
-from apps.stable_diffusion.src.utils.sd_annotation import (
-    get_device_args,
-    load_winograd_configs,
-)
-from apps.stable_diffusion.src.models import SharkifyStableDiffusionModel
-
-
-def load_mlir_module():
-    sd_model = SharkifyStableDiffusionModel(
-        args.hf_model_id,
-        args.ckpt_loc,
-        args.custom_vae,
-        args.precision,
-        max_len=args.max_length,
-        batch_size=args.batch_size,
-        height=args.height,
-        width=args.width,
-        use_base_vae=args.use_base_vae,
-        use_tuned=False,
-        low_cpu_mem_usage=args.low_cpu_mem_usage,
-        return_mlir=True,
-    )
-
-    if args.annotation_model == "unet":
-        mlir_module = sd_model.unet()
-        model_name = sd_model.model_name["unet"]
-    elif args.annotation_model == "vae":
-        mlir_module = sd_model.vae()
-        model_name = sd_model.model_name["vae"]
-    else:
-        raise ValueError(
-            f"{args.annotation_model} is not supported for tuning."
-        )
-
-    return mlir_module, model_name
-
-
-def main():
-    args.use_tuned = False
-    set_init_device_flags()
-    mlir_module, model_name = load_mlir_module()
-
-    # Get device and device specific arguments
-    device, device_spec_args = get_device_args()
-    device_spec = ""
-    vulkan_target_triple = ""
-    if device_spec_args:
-        device_spec = device_spec_args[-1].split("=")[-1].strip()
-        if device == "vulkan":
-            vulkan_target_triple = device_spec
-            device_spec = device_spec.split("-")[0]
-
-    # Add winograd annotation for vulkan device
-    use_winograd = (
-        True
-        if device == "vulkan" and args.annotation_model in ["unet", "vae"]
-        else False
-    )
-    winograd_config = (
-        load_winograd_configs()
-        if device == "vulkan" and args.annotation_model in ["unet", "vae"]
-        else ""
-    )
-    with create_context() as ctx:
-        input_module = model_annotation(
-            ctx,
-            input_contents=mlir_module,
-            config_path=winograd_config,
-            search_op="conv",
-            winograd=use_winograd,
-        )
-
-    # Dump model dispatches
-    generates_dir = Path.home() / "tmp"
-    if not os.path.exists(generates_dir):
-        os.makedirs(generates_dir)
-    dump_mlir = generates_dir / "temp.mlir"
-    dispatch_dir = generates_dir / f"{model_name}_{device_spec}_dispatches"
-    export_module_to_mlir_file(input_module, dump_mlir)
-    dump_dispatches(
-        dump_mlir,
-        device,
-        dispatch_dir,
-        vulkan_target_triple,
-        use_winograd=use_winograd,
-    )
-
-    # Tune each dispatch
-    dtype = "f16" if args.precision == "fp16" else "f32"
-    config_filename = f"{model_name}_{device_spec}_configs.json"
-
-    for f_path in os.listdir(dispatch_dir):
-        if not f_path.endswith(".mlir"):
-            continue
-
-        model_dir = os.path.join(dispatch_dir, f_path)
-
-        tuner = SharkCodegenTuner(
-            model_dir,
-            device,
-            "random",
-            args.num_iters,
-            args.tuned_config_dir,
-            dtype,
-            args.search_op,
-            batch_size=1,
-            config_filename=config_filename,
-            use_dispatch=True,
-            vulkan_target_triple=vulkan_target_triple,
-        )
-        tuner.tune()
-
-
-if __name__ == "__main__":
-    main()

apps/stable_diffusion/scripts/txt2img.py

@@ -1,86 +0,0 @@
-import torch
-import transformers
-import time
-from apps.stable_diffusion.src import (
-    args,
-    Text2ImagePipeline,
-    get_schedulers,
-    set_init_device_flags,
-    utils,
-    clear_all,
-    save_output_img,
-)
-
-
-def main():
-    if args.clear_all:
-        clear_all()
-
-    dtype = torch.float32 if args.precision == "fp32" else torch.half
-    cpu_scheduling = not args.scheduler.startswith("Shark")
-    set_init_device_flags()
-    schedulers = get_schedulers(args.hf_model_id)
-    scheduler_obj = schedulers[args.scheduler]
-    seed = args.seed
-    txt2img_obj = Text2ImagePipeline.from_pretrained(
-        scheduler=scheduler_obj,
-        import_mlir=args.import_mlir,
-        model_id=args.hf_model_id,
-        ckpt_loc=args.ckpt_loc,
-        precision=args.precision,
-        max_length=args.max_length,
-        batch_size=args.batch_size,
-        height=args.height,
-        width=args.width,
-        use_base_vae=args.use_base_vae,
-        use_tuned=args.use_tuned,
-        custom_vae=args.custom_vae,
-        low_cpu_mem_usage=args.low_cpu_mem_usage,
-        debug=args.import_debug if args.import_mlir else False,
-        use_lora=args.use_lora,
-        use_quantize=args.use_quantize,
-        ondemand=args.ondemand,
-    )
-
-    for current_batch in range(args.batch_count):
-        if current_batch > 0:
-            seed = -1
-        seed = utils.sanitize_seed(seed)
-
-        start_time = time.time()
-        generated_imgs = txt2img_obj.generate_images(
-            args.prompts,
-            args.negative_prompts,
-            args.batch_size,
-            args.height,
-            args.width,
-            args.steps,
-            args.guidance_scale,
-            seed,
-            args.max_length,
-            dtype,
-            args.use_base_vae,
-            cpu_scheduling,
-            args.max_embeddings_multiples,
-        )
-        total_time = time.time() - start_time
-        text_output = f"prompt={args.prompts}"
-        text_output += f"\nnegative prompt={args.negative_prompts}"
-        text_output += (
-            f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
-        )
-        text_output += f"\nscheduler={args.scheduler}, device={args.device}"
-        text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
-        text_output += (
-            f", batch size={args.batch_size}, max_length={args.max_length}"
-        )
-        # TODO: if using --batch_count=x txt2img_obj.log will output on each display every iteration infos from the start
-        text_output += txt2img_obj.log
-        text_output += f"\nTotal image generation time: {total_time:.4f}sec"
-
-        save_output_img(generated_imgs[0], seed)
-        print(text_output)
-
-
-if __name__ == "__main__":
-    main()

apps/stable_diffusion/scripts/upscaler.py

@@ -1,91 +0,0 @@
-import torch
-import time
-from PIL import Image
-import transformers
-from apps.stable_diffusion.src import (
-    args,
-    UpscalerPipeline,
-    get_schedulers,
-    set_init_device_flags,
-    utils,
-    clear_all,
-    save_output_img,
-)
-
-
-if __name__ == "__main__":
-    if args.clear_all:
-        clear_all()
-
-    if args.img_path is None:
-        print("Flag --img_path is required.")
-        exit()
-
-    # When the models get uploaded, it should be default to False.
-    args.import_mlir = True
-
-    cpu_scheduling = not args.scheduler.startswith("Shark")
-    dtype = torch.float32 if args.precision == "fp32" else torch.half
-    set_init_device_flags()
-    schedulers = get_schedulers(args.hf_model_id)
-
-    scheduler_obj = schedulers[args.scheduler]
-    image = (
-        Image.open(args.img_path)
-        .convert("RGB")
-        .resize((args.height, args.width))
-    )
-    seed = utils.sanitize_seed(args.seed)
-    # Adjust for height and width based on model
-
-    upscaler_obj = UpscalerPipeline.from_pretrained(
-        scheduler_obj,
-        args.import_mlir,
-        args.hf_model_id,
-        args.ckpt_loc,
-        args.custom_vae,
-        args.precision,
-        args.max_length,
-        args.batch_size,
-        args.height,
-        args.width,
-        args.use_base_vae,
-        args.use_tuned,
-        low_cpu_mem_usage=args.low_cpu_mem_usage,
-        use_lora=args.use_lora,
-        ddpm_scheduler=schedulers["DDPM"],
-        ondemand=args.ondemand,
-    )
-
-    start_time = time.time()
-    generated_imgs = upscaler_obj.generate_images(
-        args.prompts,
-        args.negative_prompts,
-        image,
-        args.batch_size,
-        args.height,
-        args.width,
-        args.steps,
-        args.noise_level,
-        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}, noise_level={args.noise_level}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
-    text_output += (
-        f", batch size={args.batch_size}, max_length={args.max_length}"
-    )
-    text_output += upscaler_obj.log
-    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
-
-    extra_info = {"NOISE LEVEL": args.noise_level}
-    save_output_img(generated_imgs[0], seed, extra_info)
-    print(text_output)

apps/stable_diffusion/shark_sd.spec

@@ -1,90 +0,0 @@
-# -*- mode: python ; coding: utf-8 -*-
-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)
-
-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('torch-mlir')
-datas += copy_metadata('omegaconf')
-datas += copy_metadata('safetensors')
-datas += collect_data_files('diffusers')
-datas += collect_data_files('transformers')
-datas += collect_data_files('pytorch_lightning')
-datas += collect_data_files('opencv-python')
-datas += collect_data_files('skimage')
-datas += collect_data_files('gradio')
-datas += collect_data_files('gradio_client')
-datas += collect_data_files('iree')
-datas += collect_data_files('google-cloud-storage')
-datas += collect_data_files('shark')
-datas += collect_data_files('tkinter')
-datas += collect_data_files('webview')
-datas += collect_data_files('sentencepiece')
-datas += [
-         ( 'src/utils/resources/prompts.json', 'resources' ),
-         ( 'src/utils/resources/model_db.json', 'resources' ),
-         ( 'src/utils/resources/opt_flags.json', 'resources' ),
-         ( 'src/utils/resources/base_model.json', 'resources' ),
-         ( 'web/ui/css/*', 'ui/css' ),
-         ( 'web/ui/logos/*', 'logos' )
-         ]
-
-binaries = []
-
-block_cipher = None
-
-hiddenimports = ['shark', 'shark.shark_inference', 'apps']
-hiddenimports += [x for x in collect_submodules("skimage") if "tests" not in x]
-hiddenimports += [x for x in collect_submodules("iree") if "tests" not in x]
-hiddenimports += [x for x in collect_submodules("PIL") if "tests" not in x]
-
-a = Analysis(
-    ['web/index.py'],
-    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_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

@@ -1,84 +0,0 @@
-# -*- mode: python ; coding: utf-8 -*-
-from PyInstaller.utils.hooks import collect_data_files
-from PyInstaller.utils.hooks import collect_submodules
-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('torch-mlir')
-datas += copy_metadata('omegaconf')
-datas += copy_metadata('safetensors')
-datas += collect_data_files('diffusers')
-datas += collect_data_files('transformers')
-datas += collect_data_files('opencv-python')
-datas += collect_data_files('pytorch_lightning')
-datas += collect_data_files('skimage')
-datas += collect_data_files('gradio')
-datas += collect_data_files('gradio_client')
-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
-
-hiddenimports = ['shark', 'shark.shark_inference', 'apps']
-hiddenimports += [x for x in collect_submodules("skimage") if "tests" not in x]
-hiddenimports += [x for x in collect_submodules("iree") if "tests" not in x]
-
-a = Analysis(
-    ['scripts/main.py'],
-    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_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

@@ -1,18 +0,0 @@
-from apps.stable_diffusion.src.utils import (
-    args,
-    set_init_device_flags,
-    prompt_examples,
-    get_available_devices,
-    clear_all,
-    save_output_img,
-    resize_stencil,
-)
-from apps.stable_diffusion.src.pipelines import (
-    Text2ImagePipeline,
-    Image2ImagePipeline,
-    InpaintPipeline,
-    OutpaintPipeline,
-    StencilPipeline,
-    UpscalerPipeline,
-)
-from apps.stable_diffusion.src.schedulers import get_schedulers

apps/stable_diffusion/src/models/__init__.py

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

apps/stable_diffusion/src/models/model_wrappers.py

@@ -1,703 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, ControlNetModel
-from transformers import CLIPTextModel
-from collections import defaultdict
-from pathlib import Path
-import torch
-import safetensors.torch
-import traceback
-import subprocess
-import sys
-import os
-from apps.stable_diffusion.src.utils import (
-    compile_through_fx,
-    get_opt_flags,
-    base_models,
-    args,
-    preprocessCKPT,
-    convert_original_vae,
-    get_path_to_diffusers_checkpoint,
-    fetch_and_update_base_model_id,
-    get_path_stem,
-    get_extended_name,
-    get_stencil_model_id,
-    update_lora_weight,
-)
-
-
-# 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 "*" in shape[i]:
-                mul_val = int(shape[i].split("*")[0])
-                if "batch_size" in shape[i]:
-                    new_shape.append(batch_size * mul_val)
-                elif "height" in shape[i]:
-                    new_shape.append(height * mul_val)
-                elif "width" in shape[i]:
-                    new_shape.append(width * mul_val)
-            elif "/" in shape[i]:
-                import math
-                div_val = int(shape[i].split("/")[1])
-                if "batch_size" in shape[i]:
-                    new_shape.append(math.ceil(batch_size / div_val))
-                elif "height" in shape[i]:
-                    new_shape.append(math.ceil(height / div_val))
-                elif "width" in shape[i]:
-                    new_shape.append(math.ceil(width / div_val))
-        else:
-            new_shape.append(shape[i])
-    return new_shape
-
-
-def check_compilation(model, model_name):
-    if not model:
-        raise Exception(f"Could not compile {model_name}. Please create an issue with the detailed log at https://github.com/nod-ai/SHARK/issues")
-
-
-class SharkifyStableDiffusionModel:
-    def __init__(
-        self,
-        model_id: str,
-        custom_weights: str,
-        custom_vae: str,
-        precision: str,
-        max_len: int = 64,
-        width: int = 512,
-        height: int = 512,
-        batch_size: int = 1,
-        use_base_vae: bool = False,
-        use_tuned: bool = False,
-        low_cpu_mem_usage: bool = False,
-        debug: bool = False,
-        sharktank_dir: str = "",
-        generate_vmfb: bool = True,
-        is_inpaint: bool = False,
-        is_upscaler: bool = False,
-        use_stencil: str = None,
-        use_lora: str = "",
-        use_quantize: str = None,
-        return_mlir: 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
-        self.use_quantize = use_quantize
-        if custom_weights != "":
-            if "civitai" in custom_weights:
-                weights_id = custom_weights.split("/")[-1]
-                # TODO: use model name and identify file type by civitai rest api
-                weights_path = str(Path.cwd()) + "/models/" + weights_id + ".safetensors"
-                if not os.path.isfile(weights_path):
-                    subprocess.run(["wget", custom_weights, "-O", weights_path])
-                custom_weights = get_path_to_diffusers_checkpoint(weights_path)
-                self.custom_weights = weights_path
-            else:
-                assert custom_weights.lower().endswith(
-                    (".ckpt", ".safetensors")
-                ), "checkpoint files supported can be any of [.ckpt, .safetensors] type"
-                custom_weights = get_path_to_diffusers_checkpoint(custom_weights)
-        self.model_id = model_id if custom_weights == "" else custom_weights
-        # TODO: remove the following line when stable-diffusion-2-1 works
-        if self.model_id == "stabilityai/stable-diffusion-2-1":
-            self.model_id = "stabilityai/stable-diffusion-2-1-base"
-        self.custom_vae = custom_vae
-        self.precision = precision
-        self.base_vae = use_base_vae
-        self.model_name = (
-            "_"
-            + str(batch_size)
-            + "_"
-            + str(max_len)
-            + "_"
-            + str(height)
-            + "_"
-            + str(width)
-            + "_"
-            + precision
-        )
-        print(f'use_tuned? sharkify: {use_tuned}')
-        self.use_tuned = use_tuned
-        if use_tuned:
-            self.model_name = self.model_name + "_tuned"
-        self.model_name = self.model_name + "_" + get_path_stem(self.model_id)
-        self.low_cpu_mem_usage = low_cpu_mem_usage
-        self.is_inpaint = is_inpaint
-        self.is_upscaler = is_upscaler
-        self.use_stencil = get_stencil_model_id(use_stencil)
-        if use_lora != "":
-            self.model_name = self.model_name + "_" + get_path_stem(use_lora)
-        self.use_lora = use_lora
-
-        print(self.model_name)
-        self.model_name = self.get_extended_name_for_all_model()
-        self.debug = debug
-        self.sharktank_dir = sharktank_dir
-        self.generate_vmfb = generate_vmfb
-
-        self.inputs = dict()
-        self.model_to_run = ""
-        if self.custom_weights != "":
-            self.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, self.is_inpaint)
-        else:
-            self.model_to_run = args.hf_model_id
-        self.custom_vae = self.process_custom_vae()
-        self.base_model_id = fetch_and_update_base_model_id(self.model_to_run)
-        if self.base_model_id != "" and args.ckpt_loc != "":
-            args.hf_model_id = self.base_model_id
-        self.return_mlir = return_mlir
-
-    def get_extended_name_for_all_model(self):
-        model_name = {}
-        sub_model_list = ["clip", "unet", "unet512", "stencil_unet", "vae", "vae_encode", "stencil_adaptor"]
-        index = 0
-        for model in sub_model_list:
-            sub_model = model
-            model_config = self.model_name
-            if "vae" == model:
-                if self.custom_vae != "":
-                    model_config = model_config + get_path_stem(self.custom_vae)
-                if self.base_vae:
-                    sub_model = "base_vae"
-            if "stencil_adaptor" == model and self.use_stencil is not None:
-                model_config = model_config + get_path_stem(self.use_stencil)
-            model_name[model] = get_extended_name(sub_model + model_config)
-            index += 1
-        return model_name
-
-    def check_params(self, max_len, width, height):
-        if not (max_len >= 32 and max_len <= 77):
-            sys.exit("please specify max_len in the range [32, 77].")
-        if not (width % 8 == 0 and width >= 128):
-            sys.exit("width should be greater than 128 and multiple of 8")
-        if not (height % 8 == 0 and height >= 128):
-            sys.exit("height should be greater than 128 and multiple of 8")
-
-    # Get the input info for a model i.e. "unet", "clip", "vae", etc.
-    def get_input_info_for(self, model_info):
-        dtype_config = {"f32": torch.float32, "i64": torch.int64}
-        input_map = []
-        for inp in model_info:
-            shape = model_info[inp]["shape"]
-            dtype = dtype_config[model_info[inp]["dtype"]]
-            tensor = None
-            if isinstance(shape, list):
-                clean_shape = replace_shape_str(
-                    shape, self.max_len, self.width, self.height, self.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.append(tensor)
-        return input_map
-    
-    def get_vae_encode(self):
-        class VaeEncodeModel(torch.nn.Module):
-            def __init__(self, model_id=self.model_id, low_cpu_mem_usage=False):
-                super().__init__()
-                self.vae = AutoencoderKL.from_pretrained(
-                    model_id,
-                    subfolder="vae",
-                    low_cpu_mem_usage=low_cpu_mem_usage,
-                )
-
-            def forward(self, input):
-                latents = self.vae.encode(input).latent_dist.sample()
-                return 0.18215 * latents
-
-        vae_encode = VaeEncodeModel()
-        inputs = tuple(self.inputs["vae_encode"])
-        is_f16 = True if not self.is_upscaler and self.precision == "fp16" else False
-        shark_vae_encode, vae_encode_mlir = compile_through_fx(
-            vae_encode,
-            inputs,
-            is_f16=is_f16,
-            use_tuned=self.use_tuned,
-            extended_model_name=self.model_name["vae_encode"],
-            extra_args=get_opt_flags("vae", precision=self.precision),
-            base_model_id=self.base_model_id,
-            model_name="vae_encode",
-            precision=self.precision,
-            return_mlir=self.return_mlir,
-        )
-        return shark_vae_encode, vae_encode_mlir
-
-    def get_vae(self):
-        class VaeModel(torch.nn.Module):
-            def __init__(self, model_id=self.model_id, base_vae=self.base_vae, custom_vae=self.custom_vae, low_cpu_mem_usage=False):
-                super().__init__()
-                self.vae = None
-                if custom_vae == "":
-                    self.vae = AutoencoderKL.from_pretrained(
-                        model_id,
-                        subfolder="vae",
-                        low_cpu_mem_usage=low_cpu_mem_usage,
-                    )
-                elif not isinstance(custom_vae, dict):
-                    self.vae = AutoencoderKL.from_pretrained(
-                        custom_vae,
-                        subfolder="vae",
-                        low_cpu_mem_usage=low_cpu_mem_usage,
-                    )
-                else:
-                    self.vae = AutoencoderKL.from_pretrained(
-                        model_id,
-                        subfolder="vae",
-                        low_cpu_mem_usage=low_cpu_mem_usage,
-                    )
-                    self.vae.load_state_dict(custom_vae)
-                self.base_vae = base_vae
-
-            def forward(self, input):
-                if not self.base_vae:
-                    input = 1 / 0.18215 * input
-                x = self.vae.decode(input, return_dict=False)[0]
-                x = (x / 2 + 0.5).clamp(0, 1)
-                if self.base_vae:
-                    return x
-                x = x * 255.0
-                return x.round()
-
-        vae = VaeModel(low_cpu_mem_usage=self.low_cpu_mem_usage)
-        inputs = tuple(self.inputs["vae"])
-        is_f16 = True if not self.is_upscaler and self.precision == "fp16" else False
-        save_dir = os.path.join(self.sharktank_dir, self.model_name["vae"])
-        if self.debug:
-            os.makedirs(save_dir, exist_ok=True)
-        shark_vae, vae_mlir = compile_through_fx(
-            vae,
-            inputs,
-            is_f16=is_f16,
-            use_tuned=self.use_tuned,
-            extended_model_name=self.model_name["vae"],
-            debug=self.debug,
-            generate_vmfb=self.generate_vmfb,
-            save_dir=save_dir,
-            extra_args=get_opt_flags("vae", precision=self.precision),
-            base_model_id=self.base_model_id,
-            model_name="vae",
-            precision=self.precision,
-            return_mlir=self.return_mlir,
-        )
-        return shark_vae, vae_mlir
-
-    def get_controlled_unet(self):
-        class ControlledUnetModel(torch.nn.Module):
-            def __init__(
-                self, model_id=self.model_id, low_cpu_mem_usage=False, use_lora=self.use_lora
-            ):
-                super().__init__()
-                self.unet = UNet2DConditionModel.from_pretrained(
-                    model_id,
-                    subfolder="unet",
-                    low_cpu_mem_usage=low_cpu_mem_usage,
-                )
-                if use_lora != "":
-                    update_lora_weight(self.unet, use_lora, "unet")
-                self.in_channels = self.unet.in_channels
-                self.train(False)
-
-            def forward( self, latent, timestep, text_embedding, guidance_scale, control1,
-                         control2, control3, control4, control5, control6, control7,
-                         control8, control9, control10, control11, control12, control13,
-            ):
-                # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
-                db_res_samples = tuple([ control1, control2, control3, control4, control5, control6, control7, control8, control9, control10, control11, control12,])
-                mb_res_samples = control13
-                latents = torch.cat([latent] * 2)
-                unet_out = self.unet.forward(
-                    latents,
-                    timestep,
-                    encoder_hidden_states=text_embedding,
-                    down_block_additional_residuals=db_res_samples,
-                    mid_block_additional_residual=mb_res_samples,
-                    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 = ControlledUnetModel(low_cpu_mem_usage=self.low_cpu_mem_usage)
-        is_f16 = True if self.precision == "fp16" else False
-
-        inputs = tuple(self.inputs["unet"])
-        input_mask = [True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True,]
-        shark_controlled_unet, controlled_unet_mlir = compile_through_fx(
-            unet,
-            inputs,
-            extended_model_name=self.model_name["stencil_unet"],
-            is_f16=is_f16,
-            f16_input_mask=input_mask,
-            use_tuned=self.use_tuned,
-            extra_args=get_opt_flags("unet", precision=self.precision),
-            base_model_id=self.base_model_id,
-            model_name="stencil_unet",
-            precision=self.precision,
-            return_mlir=self.return_mlir,
-        )
-        return shark_controlled_unet, controlled_unet_mlir
-
-    def get_control_net(self):
-        class StencilControlNetModel(torch.nn.Module):
-            def __init__(
-                self, model_id=self.use_stencil, low_cpu_mem_usage=False
-            ):
-                super().__init__()
-                self.cnet = ControlNetModel.from_pretrained(
-                    model_id,
-                    low_cpu_mem_usage=low_cpu_mem_usage,
-                )
-                self.in_channels = self.cnet.in_channels
-                self.train(False)
-
-            def forward(
-                self,
-                latent,
-                timestep,
-                text_embedding,
-                stencil_image_input,
-            ):
-                # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
-                # TODO: guidance NOT NEEDED change in `get_input_info` later
-                latents = torch.cat(
-                    [latent] * 2
-                )  # needs to be same as controlledUNET latents
-                stencil_image = torch.cat(
-                    [stencil_image_input] * 2
-                )  # needs to be same as controlledUNET latents
-                down_block_res_samples, mid_block_res_sample = self.cnet.forward(
-                    latents,
-                    timestep,
-                    encoder_hidden_states=text_embedding,
-                    controlnet_cond=stencil_image,
-                    return_dict=False,
-                )
-                return tuple(list(down_block_res_samples) + [mid_block_res_sample])
-
-        scnet = StencilControlNetModel(low_cpu_mem_usage=self.low_cpu_mem_usage)
-        is_f16 = True if self.precision == "fp16" else False
-
-        inputs = tuple(self.inputs["stencil_adaptor"])
-        input_mask = [True, True, True, True]
-        shark_cnet, cnet_mlir = compile_through_fx(
-            scnet,
-            inputs,
-            extended_model_name=self.model_name["stencil_adaptor"],
-            is_f16=is_f16,
-            f16_input_mask=input_mask,
-            use_tuned=self.use_tuned,
-            extra_args=get_opt_flags("unet", precision=self.precision),
-            base_model_id=self.base_model_id,
-            model_name="stencil_adaptor",
-            precision=self.precision,
-            return_mlir=self.return_mlir,
-        )
-        return shark_cnet, cnet_mlir
-
-    def get_unet(self, use_large=False):
-        class UnetModel(torch.nn.Module):
-            def __init__(self, model_id=self.model_id, low_cpu_mem_usage=False, use_lora=self.use_lora):
-                super().__init__()
-                self.unet = UNet2DConditionModel.from_pretrained(
-                    model_id,
-                    subfolder="unet",
-                    low_cpu_mem_usage=low_cpu_mem_usage,
-                )
-                if use_lora != "":
-                    update_lora_weight(self.unet, use_lora, "unet")
-                self.in_channels = self.unet.in_channels
-                self.train(False)
-                if(args.attention_slicing is not None and args.attention_slicing != "none"):
-                    if(args.attention_slicing.isdigit()):
-                        self.unet.set_attention_slice(int(args.attention_slicing))
-                    else:
-                        self.unet.set_attention_slice(args.attention_slicing)
-
-            # TODO: Instead of flattening the `control` try to use the list.
-            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(low_cpu_mem_usage=self.low_cpu_mem_usage)
-        is_f16 = True if self.precision == "fp16" else False
-        inputs = tuple(self.inputs["unet"])
-        if(use_large):
-            pad = (0, 0) * (len(inputs[2].shape) - 2)
-            pad = pad + (0, 512 - inputs[2].shape[1])
-            inputs = (inputs[0],
-                inputs[1],
-                torch.nn.functional.pad(inputs[2], pad),
-                inputs[3])
-            save_dir = os.path.join(self.sharktank_dir, self.model_name["unet512"])
-        else:
-            save_dir = os.path.join(self.sharktank_dir, self.model_name["unet"])
-        input_mask = [True, True, True, False]
-        if self.debug:
-            os.makedirs(
-                save_dir,
-                exist_ok=True,
-            )
-        model_name = "unet512" if use_large else "unet"
-        shark_unet, unet_mlir = compile_through_fx(
-            unet,
-            inputs,
-            extended_model_name=self.model_name[model_name],
-            is_f16=is_f16,
-            f16_input_mask=input_mask,
-            use_tuned=self.use_tuned,
-            debug=self.debug,
-            generate_vmfb=self.generate_vmfb,
-            save_dir=save_dir,
-            extra_args=get_opt_flags("unet", precision=self.precision),
-            base_model_id=self.base_model_id,
-            model_name=model_name,
-            precision=self.precision,
-            return_mlir=self.return_mlir,
-        )
-        return shark_unet, unet_mlir
-
-    def get_unet_upscaler(self, use_large=False):
-        class UnetModel(torch.nn.Module):
-            def __init__(self, model_id=self.model_id, low_cpu_mem_usage=False):
-                super().__init__()
-                self.unet = UNet2DConditionModel.from_pretrained(
-                    model_id,
-                    subfolder="unet",
-                    low_cpu_mem_usage=low_cpu_mem_usage,
-                )
-                self.in_channels = self.unet.in_channels
-                self.train(False)
-
-            def forward(self, latent, timestep, text_embedding, noise_level):
-                unet_out = self.unet.forward(
-                    latent,
-                    timestep,
-                    text_embedding,
-                    noise_level,
-                    return_dict=False,
-                )[0]
-                return unet_out
-
-        unet = UnetModel(low_cpu_mem_usage=self.low_cpu_mem_usage)
-        is_f16 = True if self.precision == "fp16" else False
-        inputs = tuple(self.inputs["unet"])
-        if(use_large):
-            pad = (0, 0) * (len(inputs[2].shape) - 2)
-            pad = pad + (0, 512 - inputs[2].shape[1])
-            inputs = (inputs[0],
-                inputs[1],
-                torch.nn.functional.pad(inputs[2], pad),
-                inputs[3])
-        input_mask = [True, True, True, False]
-        shark_unet, unet_mlir = compile_through_fx(
-            unet,
-            inputs,
-            extended_model_name=self.model_name["unet"],
-            is_f16=is_f16,
-            f16_input_mask=input_mask,
-            use_tuned=self.use_tuned,
-            extra_args=get_opt_flags("unet", precision=self.precision),
-            base_model_id=self.base_model_id,
-            model_name="unet",
-            precision=self.precision,
-            return_mlir=self.return_mlir,
-        )
-        return shark_unet, unet_mlir
-
-    def get_clip(self):
-        class CLIPText(torch.nn.Module):
-            def __init__(self, model_id=self.model_id, low_cpu_mem_usage=False, use_lora=self.use_lora):
-                super().__init__()
-                self.text_encoder = CLIPTextModel.from_pretrained(
-                    model_id,
-                    subfolder="text_encoder",
-                    low_cpu_mem_usage=low_cpu_mem_usage,
-                )
-                if use_lora != "":
-                    update_lora_weight(self.text_encoder, use_lora, "text_encoder")
-
-            def forward(self, input):
-                return self.text_encoder(input)[0]
-
-        clip_model = CLIPText(low_cpu_mem_usage=self.low_cpu_mem_usage)
-        save_dir = os.path.join(self.sharktank_dir, self.model_name["clip"])
-        if self.debug:
-            os.makedirs(
-                save_dir,
-                exist_ok=True,
-            )
-        shark_clip, clip_mlir = compile_through_fx(
-            clip_model,
-            tuple(self.inputs["clip"]),
-            extended_model_name=self.model_name["clip"],
-            debug=self.debug,
-            generate_vmfb=self.generate_vmfb,
-            save_dir=save_dir,
-            extra_args=get_opt_flags("clip", precision="fp32"),
-            base_model_id=self.base_model_id,
-            model_name="clip",
-            precision=self.precision,
-            return_mlir=self.return_mlir,
-        )
-        return shark_clip, clip_mlir
-
-    def process_custom_vae(self):
-        custom_vae = self.custom_vae.lower()
-        if not custom_vae.endswith((".ckpt", ".safetensors")):
-            return self.custom_vae
-        try:
-            preprocessCKPT(self.custom_vae)
-            return get_path_to_diffusers_checkpoint(self.custom_vae)
-        except:
-            print("Processing standalone Vae checkpoint")
-            vae_checkpoint = None
-            vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-            if custom_vae.endswith(".ckpt"):
-                vae_checkpoint = torch.load(self.custom_vae, map_location="cpu")
-            else:
-                vae_checkpoint = safetensors.torch.load_file(self.custom_vae, device="cpu")
-            if "state_dict" in vae_checkpoint:
-                vae_checkpoint = vae_checkpoint["state_dict"]
-
-            try:
-                vae_checkpoint = convert_original_vae(vae_checkpoint)
-            finally:
-                vae_dict = {k: v for k, v in vae_checkpoint.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-                return vae_dict
-
-    def compile_unet_variants(self, model, use_large=False):
-        if model == "unet":
-            if self.is_upscaler:
-                return self.get_unet_upscaler(use_large=use_large)
-            # TODO: Plug the experimental "int8" support at right place.
-            elif self.use_quantize == "int8":
-                from apps.stable_diffusion.src.models.opt_params import get_unet
-                return get_unet()
-            else:
-                return self.get_unet(use_large=use_large)
-        else:
-            return self.get_controlled_unet()
-
-    def vae_encode(self):
-        try:
-            self.inputs["vae_encode"] = self.get_input_info_for(base_models["vae_encode"])
-            compiled_vae_encode, vae_encode_mlir = self.get_vae_encode()
-
-            check_compilation(compiled_vae_encode, "Vae Encode")
-            if self.return_mlir:
-                return vae_encode_mlir
-            return compiled_vae_encode
-        except Exception as e:
-            sys.exit(e)
-
-    def clip(self):
-        try:
-            self.inputs["clip"] = self.get_input_info_for(base_models["clip"])
-            compiled_clip, clip_mlir = self.get_clip()
-
-            check_compilation(compiled_clip, "Clip")
-            if self.return_mlir:
-                return clip_mlir
-            return compiled_clip
-        except Exception as e:
-            sys.exit(e)
-
-    def unet(self, use_large=False):
-        try:
-            model = "stencil_unet" if self.use_stencil is not None else "unet"
-            compiled_unet = None
-            unet_inputs = base_models[model]
-
-            if self.base_model_id != "":
-                self.inputs["unet"] = self.get_input_info_for(unet_inputs[self.base_model_id])
-                compiled_unet, unet_mlir = self.compile_unet_variants(model, use_large=use_large)
-            else:
-                for model_id in unet_inputs:
-                    self.base_model_id = model_id
-                    self.inputs["unet"] = self.get_input_info_for(unet_inputs[model_id])
-
-                    try:
-                        compiled_unet, unet_mlir = self.compile_unet_variants(model, use_large=use_large)
-                    except Exception as e:
-                        print(e)
-                        print("Retrying with a different base model configuration")
-                        continue
-
-                    # -- Once a successful compilation has taken place we'd want to store
-                    #    the base model's configuration inferred.
-                    fetch_and_update_base_model_id(self.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
-                    break
-
-            check_compilation(compiled_unet, "Unet")
-            if self.return_mlir:
-                return unet_mlir
-            return compiled_unet
-        except Exception as e:
-            sys.exit(e)
-
-    def vae(self):
-        try:
-            vae_input = base_models["vae"]["vae_upscaler"] if self.is_upscaler else base_models["vae"]["vae"]
-            self.inputs["vae"] = self.get_input_info_for(vae_input)
-
-            is_base_vae = self.base_vae
-            if self.is_upscaler:
-                self.base_vae = True
-            compiled_vae, vae_mlir = self.get_vae()
-            self.base_vae = is_base_vae
-
-            check_compilation(compiled_vae, "Vae")
-            if self.return_mlir:
-                return vae_mlir
-            return compiled_vae
-        except Exception as e:
-            sys.exit(e)
-
-    def controlnet(self):
-        try:
-            self.inputs["stencil_adaptor"] = self.get_input_info_for(base_models["stencil_adaptor"])
-            compiled_stencil_adaptor, controlnet_mlir = self.get_control_net()
-
-            check_compilation(compiled_stencil_adaptor, "Stencil")
-            if self.return_mlir:
-                return controlnet_mlir
-            return compiled_stencil_adaptor
-        except Exception as e:
-            sys.exit(e)

apps/stable_diffusion/src/models/opt_params.py

@@ -1,123 +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", "v1_4"],
-    "dreamlike-art/dreamlike-diffusion-1.0": ["dreamlike", "v1_4"],
-    "prompthero/openjourney": ["openjourney", "v1_4"],
-    "wavymulder/Analog-Diffusion": ["analogdiffusion", "v1_4"],
-    "stabilityai/stable-diffusion-2-1": ["stablediffusion", "v2_1base"],
-    "stabilityai/stable-diffusion-2-1-base": ["stablediffusion", "v2_1base"],
-    "CompVis/stable-diffusion-v1-4": ["stablediffusion", "v1_4"],
-    "runwayml/stable-diffusion-inpainting": ["stablediffusion", "inpaint_v1"],
-    "stabilityai/stable-diffusion-2-inpainting": ["stablediffusion", "inpaint_v2"],
-}
-
-# TODO: Add the quantized model as a part model_db.json.
-# This is currently in experimental phase.
-def get_quantize_model():
-    bucket_key = "gs://shark_tank/prashant_nod"
-    model_key = "unet_int8"
-    iree_flags = get_opt_flags("unet", precision="fp16")
-    if args.height != 512 and args.width != 512 and args.max_length != 77:
-        sys.exit("The int8 quantized model currently requires the height and width to be 512, and max_length to be 77")
-    return bucket_key, model_key, iree_flags
-
-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"
-
-    # TODO: Get the quantize model from model_db.json
-    if args.use_quantize == "int8":
-        bk, mk, flags = get_quantize_model()
-        return get_shark_model(bk, mk, flags)
-
-    if "vulkan" not in args.device and args.use_tuned:
-        bucket_key = f"{variant}/{is_tuned}/{args.device}"
-        model_key = f"{variant}/{version}/unet/{args.precision}/length_{args.max_length}/{is_tuned}/{args.device}"
-    else:
-        bucket_key = f"{variant}/{is_tuned}"
-        model_key = f"{variant}/{version}/unet/{args.precision}/length_{args.max_length}/{is_tuned}"
-
-    bucket, model_name, iree_flags = get_params(
-        bucket_key, model_key, "unet", is_tuned, args.precision
-    )
-    return get_shark_model(bucket, model_name, iree_flags)
-
-
-def get_vae_encode():
-    variant, version = get_variant_version(args.hf_model_id)
-    # Tuned model is present only for `fp16` precision.
-    is_tuned = "tuned" if args.use_tuned else "untuned"
-    if "vulkan" not in args.device and args.use_tuned:
-        bucket_key = f"{variant}/{is_tuned}/{args.device}"
-        model_key = f"{variant}/{version}/vae_encode/{args.precision}/length_77/{is_tuned}/{args.device}"
-    else:
-        bucket_key = f"{variant}/{is_tuned}"
-        model_key = f"{variant}/{version}/vae_encode/{args.precision}/length_77/{is_tuned}"
-
-    bucket, model_name, iree_flags = get_params(
-        bucket_key, model_key, "vae", is_tuned, args.precision
-    )
-    return get_shark_model(bucket, model_name, iree_flags)
-
-
-def get_vae():
-    variant, version = get_variant_version(args.hf_model_id)
-    # Tuned model is present only for `fp16` precision.
-    is_tuned = "tuned" if args.use_tuned else "untuned"
-    is_base = "/base" if args.use_base_vae else ""
-    if "vulkan" not in args.device and args.use_tuned:
-        bucket_key = f"{variant}/{is_tuned}/{args.device}"
-        model_key = f"{variant}/{version}/vae/{args.precision}/length_77/{is_tuned}{is_base}/{args.device}"
-    else:
-        bucket_key = f"{variant}/{is_tuned}"
-        model_key = f"{variant}/{version}/vae/{args.precision}/length_77/{is_tuned}{is_base}"
-
-    bucket, model_name, iree_flags = get_params(
-        bucket_key, model_key, "vae", is_tuned, args.precision
-    )
-    return get_shark_model(bucket, model_name, iree_flags)
-
-
-def get_clip():
-    variant, version = get_variant_version(args.hf_model_id)
-    bucket_key = f"{variant}/untuned"
-    model_key = (
-        f"{variant}/{version}/clip/fp32/length_{args.max_length}/untuned"
-    )
-    bucket, model_name, iree_flags = get_params(
-        bucket_key, model_key, "clip", "untuned", "fp32"
-    )
-    return get_shark_model(bucket, model_name, iree_flags)
-
-
-def get_tokenizer():
-    tokenizer = CLIPTokenizer.from_pretrained(
-        args.hf_model_id, subfolder="tokenizer"
-    )
-    return tokenizer

apps/stable_diffusion/src/pipelines/__init__.py

@@ -1,18 +0,0 @@
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_txt2img import (
-    Text2ImagePipeline,
-)
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_img2img import (
-    Image2ImagePipeline,
-)
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_inpaint import (
-    InpaintPipeline,
-)
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_outpaint import (
-    OutpaintPipeline,
-)
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_stencil import (
-    StencilPipeline,
-)
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_upscaler import (
-    UpscalerPipeline,
-)

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_img2img.py

@@ -1,200 +0,0 @@
-import torch
-import time
-import numpy as np
-from tqdm.auto import tqdm
-from random import randint
-from PIL import Image
-from transformers import CLIPTokenizer
-from typing import Union
-from shark.shark_inference import SharkInference
-from diffusers import (
-    DDIMScheduler,
-    PNDMScheduler,
-    LMSDiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler,
-    DEISMultistepScheduler,
-)
-from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
-    StableDiffusionPipeline,
-)
-from apps.stable_diffusion.src.models import (
-    SharkifyStableDiffusionModel,
-    get_vae_encode,
-)
-
-
-class Image2ImagePipeline(StableDiffusionPipeline):
-    def __init__(
-        self,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-            SharkEulerDiscreteScheduler,
-            DEISMultistepScheduler,
-        ],
-        sd_model: SharkifyStableDiffusionModel,
-        import_mlir: bool,
-        use_lora: str,
-        ondemand: bool,
-    ):
-        super().__init__(scheduler, sd_model, import_mlir, use_lora, ondemand)
-        self.vae_encode = None
-
-    def load_vae_encode(self):
-        if self.vae_encode is not None:
-            return
-
-        if self.import_mlir or self.use_lora:
-            self.vae_encode = self.sd_model.vae_encode()
-        else:
-            try:
-                self.vae_encode = get_vae_encode()
-            except:
-                print("download pipeline failed, falling back to import_mlir")
-                self.vae_encode = self.sd_model.vae_encode()
-
-    def unload_vae_encode(self):
-        del self.vae_encode
-        self.vae_encode = None
-
-    def prepare_image_latents(
-        self,
-        image,
-        batch_size,
-        height,
-        width,
-        generator,
-        num_inference_steps,
-        strength,
-        dtype,
-    ):
-        # Pre process image -> get image encoded -> process latents
-
-        # TODO: process with variable HxW combos
-
-        # Pre process image
-        image = image.resize((width, height))
-        image_arr = np.stack([np.array(i) for i in (image,)], axis=0)
-        image_arr = image_arr / 255.0
-        image_arr = torch.from_numpy(image_arr).permute(0, 3, 1, 2).to(dtype)
-        image_arr = 2 * (image_arr - 0.5)
-
-        # set scheduler steps
-        self.scheduler.set_timesteps(num_inference_steps)
-        init_timestep = min(
-            int(num_inference_steps * strength), num_inference_steps
-        )
-        t_start = max(num_inference_steps - init_timestep, 0)
-        # timesteps reduced as per strength
-        timesteps = self.scheduler.timesteps[t_start:]
-        # new number of steps to be used as per strength will be
-        # num_inference_steps = num_inference_steps - t_start
-
-        # image encode
-        latents = self.encode_image((image_arr,))
-        latents = torch.from_numpy(latents).to(dtype)
-        # add noise to data
-        noise = torch.randn(latents.shape, generator=generator, dtype=dtype)
-        latents = self.scheduler.add_noise(
-            latents, noise, timesteps[0].repeat(1)
-        )
-
-        return latents, timesteps
-
-    def encode_image(self, input_image):
-        self.load_vae_encode()
-        vae_encode_start = time.time()
-        latents = self.vae_encode("forward", input_image)
-        vae_inf_time = (time.time() - vae_encode_start) * 1000
-        if self.ondemand:
-            self.unload_vae_encode()
-        self.log += f"\nVAE Encode Inference time (ms): {vae_inf_time:.3f}"
-
-        return latents
-
-    def generate_images(
-        self,
-        prompts,
-        neg_prompts,
-        image,
-        batch_size,
-        height,
-        width,
-        num_inference_steps,
-        strength,
-        guidance_scale,
-        seed,
-        max_length,
-        dtype,
-        use_base_vae,
-        cpu_scheduling,
-        use_stencil,
-    ):
-        # prompts and negative prompts must be a list.
-        if isinstance(prompts, str):
-            prompts = [prompts]
-
-        if isinstance(neg_prompts, str):
-            neg_prompts = [neg_prompts]
-
-        prompts = prompts * batch_size
-        neg_prompts = neg_prompts * batch_size
-
-        # seed generator to create the inital latent noise. Also handle out of range seeds.
-        uint32_info = np.iinfo(np.uint32)
-        uint32_min, uint32_max = uint32_info.min, uint32_info.max
-        if seed < uint32_min or seed >= uint32_max:
-            seed = randint(uint32_min, uint32_max)
-        generator = torch.manual_seed(seed)
-
-        # Get text embeddings with weight emphasis from prompts
-        text_embeddings = self.encode_prompts_weight(
-            prompts, neg_prompts, max_length
-        )
-
-        # guidance scale as a float32 tensor.
-        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
-
-        # Prepare input image latent
-        image_latents, final_timesteps = self.prepare_image_latents(
-            image=image,
-            batch_size=batch_size,
-            height=height,
-            width=width,
-            generator=generator,
-            num_inference_steps=num_inference_steps,
-            strength=strength,
-            dtype=dtype,
-        )
-
-        # Get Image latents
-        latents = self.produce_img_latents(
-            latents=image_latents,
-            text_embeddings=text_embeddings,
-            guidance_scale=guidance_scale,
-            total_timesteps=final_timesteps,
-            dtype=dtype,
-            cpu_scheduling=cpu_scheduling,
-        )
-
-        # Img latents -> PIL images
-        all_imgs = []
-        self.load_vae()
-        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)
-        if self.ondemand:
-            self.unload_vae()
-
-        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_inpaint.py

@@ -1,473 +0,0 @@
-import torch
-from tqdm.auto import tqdm
-import numpy as np
-from random import randint
-from PIL import Image, ImageOps
-from transformers import CLIPTokenizer
-from typing import Union
-from shark.shark_inference import SharkInference
-from diffusers import (
-    DDIMScheduler,
-    PNDMScheduler,
-    LMSDiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler,
-    DEISMultistepScheduler,
-)
-from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
-    StableDiffusionPipeline,
-)
-from apps.stable_diffusion.src.models import (
-    SharkifyStableDiffusionModel,
-    get_vae_encode,
-)
-
-
-class InpaintPipeline(StableDiffusionPipeline):
-    def __init__(
-        self,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-            SharkEulerDiscreteScheduler,
-            DEISMultistepScheduler,
-        ],
-        sd_model: SharkifyStableDiffusionModel,
-        import_mlir: bool,
-        use_lora: str,
-        ondemand: bool,
-    ):
-        super().__init__(scheduler, sd_model, import_mlir, use_lora, ondemand)
-        self.vae_encode = None
-
-    def load_vae_encode(self):
-        if self.vae_encode is not None:
-            return
-
-        if self.import_mlir or self.use_lora:
-            self.vae_encode = self.sd_model.vae_encode()
-        else:
-            try:
-                self.vae_encode = get_vae_encode()
-            except:
-                print("download pipeline failed, falling back to import_mlir")
-                self.vae_encode = self.sd_model.vae_encode()
-
-    def unload_vae_encode(self):
-        del self.vae_encode
-        self.vae_encode = None
-
-    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)
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    def get_crop_region(self, mask, pad=0):
-        h, w = mask.shape
-
-        crop_left = 0
-        for i in range(w):
-            if not (mask[:, i] == 0).all():
-                break
-            crop_left += 1
-
-        crop_right = 0
-        for i in reversed(range(w)):
-            if not (mask[:, i] == 0).all():
-                break
-            crop_right += 1
-
-        crop_top = 0
-        for i in range(h):
-            if not (mask[i] == 0).all():
-                break
-            crop_top += 1
-
-        crop_bottom = 0
-        for i in reversed(range(h)):
-            if not (mask[i] == 0).all():
-                break
-            crop_bottom += 1
-
-        return (
-            int(max(crop_left - pad, 0)),
-            int(max(crop_top - pad, 0)),
-            int(min(w - crop_right + pad, w)),
-            int(min(h - crop_bottom + pad, h)),
-        )
-
-    def expand_crop_region(
-        self,
-        crop_region,
-        processing_width,
-        processing_height,
-        image_width,
-        image_height,
-    ):
-        x1, y1, x2, y2 = crop_region
-
-        ratio_crop_region = (x2 - x1) / (y2 - y1)
-        ratio_processing = processing_width / processing_height
-
-        if ratio_crop_region > ratio_processing:
-            desired_height = (x2 - x1) / ratio_processing
-            desired_height_diff = int(desired_height - (y2 - y1))
-            y1 -= desired_height_diff // 2
-            y2 += desired_height_diff - desired_height_diff // 2
-            if y2 >= image_height:
-                diff = y2 - image_height
-                y2 -= diff
-                y1 -= diff
-            if y1 < 0:
-                y2 -= y1
-                y1 -= y1
-            if y2 >= image_height:
-                y2 = image_height
-        else:
-            desired_width = (y2 - y1) * ratio_processing
-            desired_width_diff = int(desired_width - (x2 - x1))
-            x1 -= desired_width_diff // 2
-            x2 += desired_width_diff - desired_width_diff // 2
-            if x2 >= image_width:
-                diff = x2 - image_width
-                x2 -= diff
-                x1 -= diff
-            if x1 < 0:
-                x2 -= x1
-                x1 -= x1
-            if x2 >= image_width:
-                x2 = image_width
-
-        return x1, y1, x2, y2
-
-    def resize_image(self, resize_mode, im, width, height):
-        """
-        resize_mode:
-            0: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.
-            1: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.
-        """
-
-        if resize_mode == 0:
-            ratio = width / height
-            src_ratio = im.width / im.height
-
-            src_w = (
-                width if ratio > src_ratio else im.width * height // im.height
-            )
-            src_h = (
-                height if ratio <= src_ratio else im.height * width // im.width
-            )
-
-            resized = im.resize((src_w, src_h), resample=Image.LANCZOS)
-            res = Image.new("RGB", (width, height))
-            res.paste(
-                resized,
-                box=(width // 2 - src_w // 2, height // 2 - src_h // 2),
-            )
-
-        else:
-            ratio = width / height
-            src_ratio = im.width / im.height
-
-            src_w = (
-                width if ratio < src_ratio else im.width * height // im.height
-            )
-            src_h = (
-                height if ratio >= src_ratio else im.height * width // im.width
-            )
-
-            resized = im.resize((src_w, src_h), resample=Image.LANCZOS)
-            res = Image.new("RGB", (width, height))
-            res.paste(
-                resized,
-                box=(width // 2 - src_w // 2, height // 2 - src_h // 2),
-            )
-
-            if ratio < src_ratio:
-                fill_height = height // 2 - src_h // 2
-                res.paste(
-                    resized.resize((width, fill_height), box=(0, 0, width, 0)),
-                    box=(0, 0),
-                )
-                res.paste(
-                    resized.resize(
-                        (width, fill_height),
-                        box=(0, resized.height, width, resized.height),
-                    ),
-                    box=(0, fill_height + src_h),
-                )
-            elif ratio > src_ratio:
-                fill_width = width // 2 - src_w // 2
-                res.paste(
-                    resized.resize(
-                        (fill_width, height), box=(0, 0, 0, height)
-                    ),
-                    box=(0, 0),
-                )
-                res.paste(
-                    resized.resize(
-                        (fill_width, height),
-                        box=(resized.width, 0, resized.width, height),
-                    ),
-                    box=(fill_width + src_w, 0),
-                )
-
-        return res
-
-    def prepare_mask_and_masked_image(
-        self,
-        image,
-        mask,
-        height,
-        width,
-        inpaint_full_res,
-        inpaint_full_res_padding,
-    ):
-        # preprocess image
-        image = image.resize((width, height))
-        mask = mask.resize((width, height))
-
-        paste_to = ()
-        overlay_image = None
-        if inpaint_full_res:
-            # prepare overlay image
-            overlay_image = Image.new("RGB", (image.width, image.height))
-            overlay_image.paste(
-                image.convert("RGB"),
-                mask=ImageOps.invert(mask.convert("L")),
-            )
-
-            # prepare mask
-            mask = mask.convert("L")
-            crop_region = self.get_crop_region(
-                np.array(mask), inpaint_full_res_padding
-            )
-            crop_region = self.expand_crop_region(
-                crop_region, width, height, mask.width, mask.height
-            )
-            x1, y1, x2, y2 = crop_region
-            mask = mask.crop(crop_region)
-            mask = self.resize_image(1, mask, width, height)
-            paste_to = (x1, y1, x2 - x1, y2 - y1)
-
-            # prepare image
-            image = image.crop(crop_region)
-            image = self.resize_image(1, image, width, height)
-
-        if isinstance(image, (Image.Image, np.ndarray)):
-            image = [image]
-
-        if isinstance(image, list) and isinstance(image[0], Image.Image):
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, (Image.Image, np.ndarray)):
-            mask = [mask]
-
-        if isinstance(mask, list) and isinstance(mask[0], Image.Image):
-            mask = np.concatenate(
-                [np.array(m.convert("L"))[None, None, :] for m in mask], axis=0
-            )
-            mask = mask.astype(np.float32) / 255.0
-        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-        mask = torch.from_numpy(mask)
-
-        masked_image = image * (mask < 0.5)
-
-        return mask, masked_image, paste_to, overlay_image
-
-    def prepare_mask_latents(
-        self,
-        mask,
-        masked_image,
-        batch_size,
-        height,
-        width,
-        dtype,
-    ):
-        mask = torch.nn.functional.interpolate(
-            mask, size=(height // 8, width // 8)
-        )
-        mask = mask.to(dtype)
-
-        self.load_vae_encode()
-        masked_image = masked_image.to(dtype)
-        masked_image_latents = self.vae_encode("forward", (masked_image,))
-        masked_image_latents = torch.from_numpy(masked_image_latents)
-        if self.ondemand:
-            self.unload_vae_encode()
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(
-                batch_size // masked_image_latents.shape[0], 1, 1, 1
-            )
-        return mask, masked_image_latents
-
-    def apply_overlay(self, image, paste_loc, overlay):
-        x, y, w, h = paste_loc
-        image = self.resize_image(0, image, w, h)
-        overlay.paste(image, (x, y))
-
-        return overlay
-
-    def generate_images(
-        self,
-        prompts,
-        neg_prompts,
-        image,
-        mask_image,
-        batch_size,
-        height,
-        width,
-        inpaint_full_res,
-        inpaint_full_res_padding,
-        num_inference_steps,
-        guidance_scale,
-        seed,
-        max_length,
-        dtype,
-        use_base_vae,
-        cpu_scheduling,
-    ):
-        # prompts and negative prompts must be a list.
-        if isinstance(prompts, str):
-            prompts = [prompts]
-
-        if isinstance(neg_prompts, str):
-            neg_prompts = [neg_prompts]
-
-        prompts = prompts * batch_size
-        neg_prompts = neg_prompts * batch_size
-
-        # seed generator to create the inital latent noise. Also handle out of range seeds.
-        uint32_info = np.iinfo(np.uint32)
-        uint32_min, uint32_max = uint32_info.min, uint32_info.max
-        if seed < uint32_min or seed >= uint32_max:
-            seed = randint(uint32_min, uint32_max)
-        generator = torch.manual_seed(seed)
-
-        # Get initial latents
-        init_latents = self.prepare_latents(
-            batch_size=batch_size,
-            height=height,
-            width=width,
-            generator=generator,
-            num_inference_steps=num_inference_steps,
-            dtype=dtype,
-        )
-
-        # Get text embeddings with weight emphasis from prompts
-        text_embeddings = self.encode_prompts_weight(
-            prompts, neg_prompts, max_length
-        )
-
-        # guidance scale as a float32 tensor.
-        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
-
-        # Preprocess mask and image
-        (
-            mask,
-            masked_image,
-            paste_to,
-            overlay_image,
-        ) = self.prepare_mask_and_masked_image(
-            image,
-            mask_image,
-            height,
-            width,
-            inpaint_full_res,
-            inpaint_full_res_padding,
-        )
-
-        # Prepare mask latent variables
-        mask, masked_image_latents = self.prepare_mask_latents(
-            mask=mask,
-            masked_image=masked_image,
-            batch_size=batch_size,
-            height=height,
-            width=width,
-            dtype=dtype,
-        )
-
-        # Get Image latents
-        latents = self.produce_img_latents(
-            latents=init_latents,
-            text_embeddings=text_embeddings,
-            guidance_scale=guidance_scale,
-            total_timesteps=self.scheduler.timesteps,
-            dtype=dtype,
-            cpu_scheduling=cpu_scheduling,
-            mask=mask,
-            masked_image_latents=masked_image_latents,
-        )
-
-        # Img latents -> PIL images
-        all_imgs = []
-        self.load_vae()
-        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)
-        if self.ondemand:
-            self.unload_vae()
-
-        if inpaint_full_res:
-            output_image = self.apply_overlay(
-                all_imgs[0], paste_to, overlay_image
-            )
-            return [output_image]
-
-        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_outpaint.py

@@ -1,567 +0,0 @@
-import torch
-from tqdm.auto import tqdm
-import numpy as np
-from random import randint
-from PIL import Image, ImageDraw, ImageFilter
-from transformers import CLIPTokenizer
-from typing import Union
-from shark.shark_inference import SharkInference
-from diffusers import (
-    DDIMScheduler,
-    PNDMScheduler,
-    LMSDiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler,
-    DEISMultistepScheduler,
-)
-from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
-    StableDiffusionPipeline,
-)
-import math
-from apps.stable_diffusion.src.models import (
-    SharkifyStableDiffusionModel,
-    get_vae_encode,
-)
-
-
-class OutpaintPipeline(StableDiffusionPipeline):
-    def __init__(
-        self,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-            SharkEulerDiscreteScheduler,
-            DEISMultistepScheduler,
-        ],
-        sd_model: SharkifyStableDiffusionModel,
-        import_mlir: bool,
-        use_lora: str,
-        ondemand: bool,
-    ):
-        super().__init__(scheduler, sd_model, import_mlir, use_lora, ondemand)
-        self.vae_encode = None
-
-    def load_vae_encode(self):
-        if self.vae_encode is not None:
-            return
-
-        if self.import_mlir or self.use_lora:
-            self.vae_encode = self.sd_model.vae_encode()
-        else:
-            try:
-                self.vae_encode = get_vae_encode()
-            except:
-                print("download pipeline failed, falling back to import_mlir")
-                self.vae_encode = self.sd_model.vae_encode()
-
-    def unload_vae_encode(self):
-        del self.vae_encode
-        self.vae_encode = None
-
-    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)
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    def prepare_mask_and_masked_image(
-        self, image, mask, mask_blur, width, height
-    ):
-        if mask_blur > 0:
-            mask = mask.filter(ImageFilter.GaussianBlur(mask_blur))
-        image = image.resize((width, height))
-        mask = mask.resize((width, height))
-
-        # preprocess image
-        if isinstance(image, (Image.Image, np.ndarray)):
-            image = [image]
-
-        if isinstance(image, list) and isinstance(image[0], Image.Image):
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, (Image.Image, np.ndarray)):
-            mask = [mask]
-
-        if isinstance(mask, list) and isinstance(mask[0], Image.Image):
-            mask = np.concatenate(
-                [np.array(m.convert("L"))[None, None, :] for m in mask], axis=0
-            )
-            mask = mask.astype(np.float32) / 255.0
-        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-        mask = torch.from_numpy(mask)
-
-        masked_image = image * (mask < 0.5)
-
-        return mask, masked_image
-
-    def prepare_mask_latents(
-        self,
-        mask,
-        masked_image,
-        batch_size,
-        height,
-        width,
-        dtype,
-    ):
-        mask = torch.nn.functional.interpolate(
-            mask, size=(height // 8, width // 8)
-        )
-        mask = mask.to(dtype)
-
-        self.load_vae_encode()
-        masked_image = masked_image.to(dtype)
-        masked_image_latents = self.vae_encode("forward", (masked_image,))
-        masked_image_latents = torch.from_numpy(masked_image_latents)
-        if self.ondemand:
-            self.unload_vae_encode()
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(
-                batch_size // masked_image_latents.shape[0], 1, 1, 1
-            )
-        return mask, masked_image_latents
-
-    def get_matched_noise(
-        self, _np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05
-    ):
-        # helper fft routines that keep ortho normalization and auto-shift before and after fft
-        def _fft2(data):
-            if data.ndim > 2:  # has channels
-                out_fft = np.zeros(
-                    (data.shape[0], data.shape[1], data.shape[2]),
-                    dtype=np.complex128,
-                )
-                for c in range(data.shape[2]):
-                    c_data = data[:, :, c]
-                    out_fft[:, :, c] = np.fft.fft2(
-                        np.fft.fftshift(c_data), norm="ortho"
-                    )
-                    out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
-            else:  # one channel
-                out_fft = np.zeros(
-                    (data.shape[0], data.shape[1]), dtype=np.complex128
-                )
-                out_fft[:, :] = np.fft.fft2(
-                    np.fft.fftshift(data), norm="ortho"
-                )
-                out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
-
-            return out_fft
-
-        def _ifft2(data):
-            if data.ndim > 2:  # has channels
-                out_ifft = np.zeros(
-                    (data.shape[0], data.shape[1], data.shape[2]),
-                    dtype=np.complex128,
-                )
-                for c in range(data.shape[2]):
-                    c_data = data[:, :, c]
-                    out_ifft[:, :, c] = np.fft.ifft2(
-                        np.fft.fftshift(c_data), norm="ortho"
-                    )
-                    out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
-            else:  # one channel
-                out_ifft = np.zeros(
-                    (data.shape[0], data.shape[1]), dtype=np.complex128
-                )
-                out_ifft[:, :] = np.fft.ifft2(
-                    np.fft.fftshift(data), norm="ortho"
-                )
-                out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
-
-            return out_ifft
-
-        def _get_gaussian_window(width, height, std=3.14, mode=0):
-            window_scale_x = float(width / min(width, height))
-            window_scale_y = float(height / min(width, height))
-
-            window = np.zeros((width, height))
-            x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x
-            for y in range(height):
-                fy = (y / height * 2.0 - 1.0) * window_scale_y
-                if mode == 0:
-                    window[:, y] = np.exp(-(x**2 + fy**2) * std)
-                else:
-                    window[:, y] = (
-                        1 / ((x**2 + 1.0) * (fy**2 + 1.0))
-                    ) ** (std / 3.14)
-
-            return window
-
-        def _get_masked_window_rgb(np_mask_grey, hardness=1.0):
-            np_mask_rgb = np.zeros(
-                (np_mask_grey.shape[0], np_mask_grey.shape[1], 3)
-            )
-            if hardness != 1.0:
-                hardened = np_mask_grey[:] ** hardness
-            else:
-                hardened = np_mask_grey[:]
-            for c in range(3):
-                np_mask_rgb[:, :, c] = hardened[:]
-            return np_mask_rgb
-
-        def _match_cumulative_cdf(source, template):
-            src_values, src_unique_indices, src_counts = np.unique(
-                source.ravel(), return_inverse=True, return_counts=True
-            )
-            tmpl_values, tmpl_counts = np.unique(
-                template.ravel(), return_counts=True
-            )
-
-            # calculate normalized quantiles for each array
-            src_quantiles = np.cumsum(src_counts) / source.size
-            tmpl_quantiles = np.cumsum(tmpl_counts) / template.size
-
-            interp_a_values = np.interp(
-                src_quantiles, tmpl_quantiles, tmpl_values
-            )
-            return interp_a_values[src_unique_indices].reshape(source.shape)
-
-        def _match_histograms(image, reference):
-            if image.ndim != reference.ndim:
-                raise ValueError(
-                    "Image and reference must have the same number of channels."
-                )
-
-            if image.shape[-1] != reference.shape[-1]:
-                raise ValueError(
-                    "Number of channels in the input image and reference image must match!"
-                )
-
-            matched = np.empty(image.shape, dtype=image.dtype)
-            for channel in range(image.shape[-1]):
-                matched_channel = _match_cumulative_cdf(
-                    image[..., channel], reference[..., channel]
-                )
-                matched[..., channel] = matched_channel
-
-            matched = matched.astype(np.float64, copy=False)
-            return matched
-
-        width = _np_src_image.shape[0]
-        height = _np_src_image.shape[1]
-        num_channels = _np_src_image.shape[2]
-
-        np_src_image = _np_src_image[:] * (1.0 - np_mask_rgb)
-        np_mask_grey = np.sum(np_mask_rgb, axis=2) / 3.0
-        img_mask = np_mask_grey > 1e-6
-        ref_mask = np_mask_grey < 1e-3
-
-        # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
-        windowed_image = _np_src_image * (
-            1.0 - _get_masked_window_rgb(np_mask_grey)
-        )
-        windowed_image /= np.max(windowed_image)
-        windowed_image += np.average(_np_src_image) * np_mask_rgb
-
-        src_fft = _fft2(
-            windowed_image
-        )  # get feature statistics from masked src img
-        src_dist = np.absolute(src_fft)
-        src_phase = src_fft / src_dist
-
-        # create a generator with a static seed to make outpainting deterministic / only follow global seed
-        rng = np.random.default_rng(0)
-
-        noise_window = _get_gaussian_window(
-            width, height, mode=1
-        )  # start with simple gaussian noise
-        noise_rgb = rng.random((width, height, num_channels))
-        noise_grey = np.sum(noise_rgb, axis=2) / 3.0
-        # the colorfulness of the starting noise is blended to greyscale with a parameter
-        noise_rgb *= color_variation
-        for c in range(num_channels):
-            noise_rgb[:, :, c] += (1.0 - color_variation) * noise_grey
-
-        noise_fft = _fft2(noise_rgb)
-        for c in range(num_channels):
-            noise_fft[:, :, c] *= noise_window
-        noise_rgb = np.real(_ifft2(noise_fft))
-        shaped_noise_fft = _fft2(noise_rgb)
-        shaped_noise_fft[:, :, :] = (
-            np.absolute(shaped_noise_fft[:, :, :]) ** 2
-            * (src_dist**noise_q)
-            * src_phase
-        )  # perform the actual shaping
-
-        # color_variation
-        brightness_variation = 0.0
-        contrast_adjusted_np_src = (
-            _np_src_image[:] * (brightness_variation + 1.0)
-            - brightness_variation * 2.0
-        )
-
-        shaped_noise = np.real(_ifft2(shaped_noise_fft))
-        shaped_noise -= np.min(shaped_noise)
-        shaped_noise /= np.max(shaped_noise)
-        shaped_noise[img_mask, :] = _match_histograms(
-            shaped_noise[img_mask, :] ** 1.0,
-            contrast_adjusted_np_src[ref_mask, :],
-        )
-        shaped_noise = (
-            _np_src_image[:] * (1.0 - np_mask_rgb) + shaped_noise * np_mask_rgb
-        )
-
-        matched_noise = shaped_noise[:]
-
-        return np.clip(matched_noise, 0.0, 1.0)
-
-    def generate_images(
-        self,
-        prompts,
-        neg_prompts,
-        image,
-        pixels,
-        mask_blur,
-        is_left,
-        is_right,
-        is_top,
-        is_bottom,
-        noise_q,
-        color_variation,
-        batch_size,
-        height,
-        width,
-        num_inference_steps,
-        guidance_scale,
-        seed,
-        max_length,
-        dtype,
-        use_base_vae,
-        cpu_scheduling,
-    ):
-        # prompts and negative prompts must be a list.
-        if isinstance(prompts, str):
-            prompts = [prompts]
-
-        if isinstance(neg_prompts, str):
-            neg_prompts = [neg_prompts]
-
-        prompts = prompts * batch_size
-        neg_prompts = neg_prompts * batch_size
-
-        # seed generator to create the inital latent noise. Also handle out of range seeds.
-        uint32_info = np.iinfo(np.uint32)
-        uint32_min, uint32_max = uint32_info.min, uint32_info.max
-        if seed < uint32_min or seed >= uint32_max:
-            seed = randint(uint32_min, uint32_max)
-        generator = torch.manual_seed(seed)
-
-        # Get initial latents
-        init_latents = self.prepare_latents(
-            batch_size=batch_size,
-            height=height,
-            width=width,
-            generator=generator,
-            num_inference_steps=num_inference_steps,
-            dtype=dtype,
-        )
-
-        # Get text embeddings with weight emphasis from prompts
-        text_embeddings = self.encode_prompts_weight(
-            prompts, neg_prompts, max_length
-        )
-
-        # guidance scale as a float32 tensor.
-        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
-
-        process_width = width
-        process_height = height
-        left = pixels if is_left else 0
-        right = pixels if is_right else 0
-        up = pixels if is_top else 0
-        down = pixels if is_bottom else 0
-        target_w = math.ceil((image.width + left + right) / 64) * 64
-        target_h = math.ceil((image.height + up + down) / 64) * 64
-
-        if left > 0:
-            left = left * (target_w - image.width) // (left + right)
-        if right > 0:
-            right = target_w - image.width - left
-        if up > 0:
-            up = up * (target_h - image.height) // (up + down)
-        if down > 0:
-            down = target_h - image.height - up
-
-        def expand(
-            init_img,
-            expand_pixels,
-            is_left=False,
-            is_right=False,
-            is_top=False,
-            is_bottom=False,
-        ):
-            is_horiz = is_left or is_right
-            is_vert = is_top or is_bottom
-            pixels_horiz = expand_pixels if is_horiz else 0
-            pixels_vert = expand_pixels if is_vert else 0
-
-            res_w = init_img.width + pixels_horiz
-            res_h = init_img.height + pixels_vert
-            process_res_w = math.ceil(res_w / 64) * 64
-            process_res_h = math.ceil(res_h / 64) * 64
-
-            img = Image.new("RGB", (process_res_w, process_res_h))
-            img.paste(
-                init_img,
-                (pixels_horiz if is_left else 0, pixels_vert if is_top else 0),
-            )
-
-            msk = Image.new("RGB", (process_res_w, process_res_h), "white")
-            draw = ImageDraw.Draw(msk)
-            draw.rectangle(
-                (
-                    expand_pixels + mask_blur if is_left else 0,
-                    expand_pixels + mask_blur if is_top else 0,
-                    msk.width - expand_pixels - mask_blur
-                    if is_right
-                    else res_w,
-                    msk.height - expand_pixels - mask_blur
-                    if is_bottom
-                    else res_h,
-                ),
-                fill="black",
-            )
-
-            np_image = (np.asarray(img) / 255.0).astype(np.float64)
-            np_mask = (np.asarray(msk) / 255.0).astype(np.float64)
-            noised = self.get_matched_noise(
-                np_image, np_mask, noise_q, color_variation
-            )
-            output_image = Image.fromarray(
-                np.clip(noised * 255.0, 0.0, 255.0).astype(np.uint8),
-                mode="RGB",
-            )
-
-            target_width = (
-                min(width, init_img.width + pixels_horiz)
-                if is_horiz
-                else img.width
-            )
-            target_height = (
-                min(height, init_img.height + pixels_vert)
-                if is_vert
-                else img.height
-            )
-            crop_region = (
-                0 if is_left else output_image.width - target_width,
-                0 if is_top else output_image.height - target_height,
-                target_width if is_left else output_image.width,
-                target_height if is_top else output_image.height,
-            )
-            mask_to_process = msk.crop(crop_region)
-            image_to_process = output_image.crop(crop_region)
-
-            # Preprocess mask and image
-            mask, masked_image = self.prepare_mask_and_masked_image(
-                image_to_process, mask_to_process, mask_blur, width, height
-            )
-
-            # Prepare mask latent variables
-            mask, masked_image_latents = self.prepare_mask_latents(
-                mask=mask,
-                masked_image=masked_image,
-                batch_size=batch_size,
-                height=height,
-                width=width,
-                dtype=dtype,
-            )
-
-            # Get Image latents
-            latents = self.produce_img_latents(
-                latents=init_latents,
-                text_embeddings=text_embeddings,
-                guidance_scale=guidance_scale,
-                total_timesteps=self.scheduler.timesteps,
-                dtype=dtype,
-                cpu_scheduling=cpu_scheduling,
-                mask=mask,
-                masked_image_latents=masked_image_latents,
-            )
-
-            # Img latents -> PIL images
-            all_imgs = []
-            self.load_vae()
-            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)
-
-            res_img = all_imgs[0].resize(
-                (image_to_process.width, image_to_process.height)
-            )
-            output_image.paste(
-                res_img,
-                (
-                    0 if is_left else output_image.width - res_img.width,
-                    0 if is_top else output_image.height - res_img.height,
-                ),
-            )
-            output_image = output_image.crop((0, 0, res_w, res_h))
-
-            return output_image
-
-        img = image.resize((width, height))
-        if left > 0:
-            img = expand(img, left, is_left=True)
-        if right > 0:
-            img = expand(img, right, is_right=True)
-        if up > 0:
-            img = expand(img, up, is_top=True)
-        if down > 0:
-            img = expand(img, down, is_bottom=True)
-
-        return [img]

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_stencil.py

@@ -1,274 +0,0 @@
-import torch
-import time
-import numpy as np
-from tqdm.auto import tqdm
-from random import randint
-from PIL import Image
-from transformers import CLIPTokenizer
-from typing import Union
-from shark.shark_inference import SharkInference
-from diffusers import (
-    DDIMScheduler,
-    PNDMScheduler,
-    LMSDiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler,
-)
-from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
-    StableDiffusionPipeline,
-)
-from apps.stable_diffusion.src.utils import controlnet_hint_conversion
-from apps.stable_diffusion.src.utils import (
-    start_profiling,
-    end_profiling,
-)
-from apps.stable_diffusion.src.models import SharkifyStableDiffusionModel
-
-
-class StencilPipeline(StableDiffusionPipeline):
-    def __init__(
-        self,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-            SharkEulerDiscreteScheduler,
-        ],
-        sd_model: SharkifyStableDiffusionModel,
-        import_mlir: bool,
-        use_lora: str,
-        ondemand: bool,
-    ):
-        super().__init__(scheduler, sd_model, import_mlir, use_lora, ondemand)
-        self.controlnet = None
-
-    def load_controlnet(self):
-        if self.controlnet is not None:
-            return
-        self.controlnet = self.sd_model.controlnet()
-
-    def unload_controlnet(self):
-        del self.controlnet
-        self.controlnet = None
-
-    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 produce_stencil_latents(
-        self,
-        latents,
-        text_embeddings,
-        guidance_scale,
-        total_timesteps,
-        dtype,
-        cpu_scheduling,
-        controlnet_hint=None,
-        controlnet_conditioning_scale: float = 1.0,
-        mask=None,
-        masked_image_latents=None,
-        return_all_latents=False,
-    ):
-        step_time_sum = 0
-        latent_history = [latents]
-        text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
-        text_embeddings_numpy = text_embeddings.detach().numpy()
-        self.load_unet()
-        self.load_controlnet()
-        for i, t in tqdm(enumerate(total_timesteps)):
-            step_start_time = time.time()
-            timestep = torch.tensor([t]).to(dtype)
-            latent_model_input = self.scheduler.scale_model_input(latents, t)
-            if mask is not None and masked_image_latents is not None:
-                latent_model_input = torch.cat(
-                    [
-                        torch.from_numpy(np.asarray(latent_model_input)),
-                        mask,
-                        masked_image_latents,
-                    ],
-                    dim=1,
-                ).to(dtype)
-            if cpu_scheduling:
-                latent_model_input = latent_model_input.detach().numpy()
-
-            if not torch.is_tensor(latent_model_input):
-                latent_model_input_1 = torch.from_numpy(
-                    np.asarray(latent_model_input)
-                ).to(dtype)
-            else:
-                latent_model_input_1 = latent_model_input
-            control = self.controlnet(
-                "forward",
-                (
-                    latent_model_input_1,
-                    timestep,
-                    text_embeddings,
-                    controlnet_hint,
-                ),
-                send_to_host=False,
-            )
-            timestep = timestep.detach().numpy()
-            # Profiling Unet.
-            profile_device = start_profiling(file_path="unet.rdc")
-            # TODO: Pass `control` as it is to Unet. Same as TODO mentioned in model_wrappers.py.
-            noise_pred = self.unet(
-                "forward",
-                (
-                    latent_model_input,
-                    timestep,
-                    text_embeddings_numpy,
-                    guidance_scale,
-                    control[0],
-                    control[1],
-                    control[2],
-                    control[3],
-                    control[4],
-                    control[5],
-                    control[6],
-                    control[7],
-                    control[8],
-                    control[9],
-                    control[10],
-                    control[11],
-                    control[12],
-                ),
-                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
-
-        if self.ondemand:
-            self.unload_unet()
-            self.unload_controlnet()
-        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
-
-    def generate_images(
-        self,
-        prompts,
-        neg_prompts,
-        image,
-        batch_size,
-        height,
-        width,
-        num_inference_steps,
-        strength,
-        guidance_scale,
-        seed,
-        max_length,
-        dtype,
-        use_base_vae,
-        cpu_scheduling,
-        use_stencil,
-    ):
-        # Control Embedding check & conversion
-        # TODO: 1. Change `num_images_per_prompt`.
-        controlnet_hint = controlnet_hint_conversion(
-            image, use_stencil, height, width, dtype, num_images_per_prompt=1
-        )
-        # prompts and negative prompts must be a list.
-        if isinstance(prompts, str):
-            prompts = [prompts]
-
-        if isinstance(neg_prompts, str):
-            neg_prompts = [neg_prompts]
-
-        prompts = prompts * batch_size
-        neg_prompts = neg_prompts * batch_size
-
-        # seed generator to create the inital latent noise. Also handle out of range seeds.
-        uint32_info = np.iinfo(np.uint32)
-        uint32_min, uint32_max = uint32_info.min, uint32_info.max
-        if seed < uint32_min or seed >= uint32_max:
-            seed = randint(uint32_min, uint32_max)
-        generator = torch.manual_seed(seed)
-
-        # Get text embeddings with weight emphasis from prompts
-        text_embeddings = self.encode_prompts_weight(
-            prompts, neg_prompts, max_length
-        )
-
-        # guidance scale as a float32 tensor.
-        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
-
-        # Prepare initial latent.
-        init_latents = self.prepare_latents(
-            batch_size=batch_size,
-            height=height,
-            width=width,
-            generator=generator,
-            num_inference_steps=num_inference_steps,
-            dtype=dtype,
-        )
-        final_timesteps = self.scheduler.timesteps
-
-        # Get Image latents
-        latents = self.produce_stencil_latents(
-            latents=init_latents,
-            text_embeddings=text_embeddings,
-            guidance_scale=guidance_scale,
-            total_timesteps=final_timesteps,
-            dtype=dtype,
-            cpu_scheduling=cpu_scheduling,
-            controlnet_hint=controlnet_hint,
-        )
-
-        # Img latents -> PIL images
-        all_imgs = []
-        self.load_vae()
-        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)
-        if self.ondemand:
-            self.unload_vae()
-
-        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_txt2img.py

@@ -1,148 +0,0 @@
-import torch
-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,
-    KDPM2DiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler,
-    DEISMultistepScheduler,
-)
-from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
-    StableDiffusionPipeline,
-)
-from apps.stable_diffusion.src.models import SharkifyStableDiffusionModel
-
-
-class Text2ImagePipeline(StableDiffusionPipeline):
-    def __init__(
-        self,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            KDPM2DiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-            SharkEulerDiscreteScheduler,
-            DEISMultistepScheduler,
-        ],
-        sd_model: SharkifyStableDiffusionModel,
-        import_mlir: bool,
-        use_lora: str,
-        ondemand: bool,
-    ):
-        super().__init__(scheduler, sd_model, import_mlir, use_lora, ondemand)
-
-    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,
-        max_embeddings_multiples,
-    ):
-        # 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 with weight emphasis from prompts
-        text_embeddings = self.encode_prompts_weight(
-            prompts,
-            neg_prompts,
-            max_length,
-            max_embeddings_multiples=max_embeddings_multiples,
-        )
-
-        # 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 = []
-        self.load_vae()
-        for i in 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)
-        if self.ondemand:
-            self.unload_vae()
-
-        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_upscaler.py

@@ -1,326 +0,0 @@
-import inspect
-import torch
-import time
-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,
-    DDPMScheduler,
-    PNDMScheduler,
-    LMSDiscreteScheduler,
-    KDPM2DiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler,
-    DEISMultistepScheduler,
-)
-from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
-from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
-    SD_STATE_IDLE,
-    SD_STATE_CANCEL,
-    StableDiffusionPipeline,
-)
-from apps.stable_diffusion.src.utils import (
-    start_profiling,
-    end_profiling,
-)
-from PIL import Image
-from apps.stable_diffusion.src.models import SharkifyStableDiffusionModel
-
-
-def preprocess(image):
-    if isinstance(image, torch.Tensor):
-        return image
-    elif isinstance(image, Image.Image):
-        image = [image]
-
-    if isinstance(image[0], Image.Image):
-        w, h = image[0].size
-        w, h = map(
-            lambda x: x - x % 64, (w, h)
-        )  # resize to integer multiple of 64
-
-        image = [np.array(i.resize((w, h)))[None, :] for i in image]
-        image = np.concatenate(image, axis=0)
-        image = np.array(image).astype(np.float32) / 255.0
-        image = image.transpose(0, 3, 1, 2)
-        image = 2.0 * image - 1.0
-        image = torch.from_numpy(image)
-    elif isinstance(image[0], torch.Tensor):
-        image = torch.cat(image, dim=0)
-    return image
-
-
-class UpscalerPipeline(StableDiffusionPipeline):
-    def __init__(
-        self,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-            SharkEulerDiscreteScheduler,
-            DEISMultistepScheduler,
-        ],
-        low_res_scheduler: Union[
-            DDIMScheduler,
-            DDPMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-            SharkEulerDiscreteScheduler,
-            DEISMultistepScheduler,
-        ],
-        sd_model: SharkifyStableDiffusionModel,
-        import_mlir: bool,
-        use_lora: str,
-        ondemand: bool,
-    ):
-        super().__init__(scheduler, sd_model, import_mlir, use_lora, ondemand)
-        self.low_res_scheduler = low_res_scheduler
-        self.status = SD_STATE_IDLE
-
-    def prepare_extra_step_kwargs(self, generator, eta):
-        accepts_eta = "eta" in set(
-            inspect.signature(self.scheduler.step).parameters.keys()
-        )
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(
-            inspect.signature(self.scheduler.step).parameters.keys()
-        )
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def decode_latents(self, latents, use_base_vae, cpu_scheduling):
-        latents = 1 / 0.08333 * (latents.float())
-        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}"
-
-        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 prepare_latents(
-        self,
-        batch_size,
-        height,
-        width,
-        generator,
-        num_inference_steps,
-        dtype,
-    ):
-        latents = torch.randn(
-            (
-                batch_size,
-                4,
-                height,
-                width,
-            ),
-            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 produce_img_latents(
-        self,
-        latents,
-        image,
-        text_embeddings,
-        guidance_scale,
-        noise_level,
-        total_timesteps,
-        dtype,
-        cpu_scheduling,
-        extra_step_kwargs,
-        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()
-        self.status = SD_STATE_IDLE
-        self.load_unet()
-        for i, t in tqdm(enumerate(total_timesteps)):
-            step_start_time = time.time()
-            latent_model_input = torch.cat([latents] * 2)
-            latent_model_input = self.scheduler.scale_model_input(
-                latent_model_input, t
-            )
-            latent_model_input = torch.cat([latent_model_input, image], dim=1)
-            timestep = torch.tensor([t]).to(dtype).detach().numpy()
-            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,
-                    noise_level,
-                ),
-            )
-            end_profiling(profile_device)
-            noise_pred = torch.from_numpy(noise_pred)
-            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-            noise_pred = noise_pred_uncond + guidance_scale * (
-                noise_pred_text - noise_pred_uncond
-            )
-
-            if cpu_scheduling:
-                latents = self.scheduler.step(
-                    noise_pred, t, latents, **extra_step_kwargs
-                ).prev_sample
-            else:
-                latents = self.scheduler.step(
-                    noise_pred, t, latents, **extra_step_kwargs
-                )
-
-            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
-
-            if self.status == SD_STATE_CANCEL:
-                break
-
-        if self.ondemand:
-            self.unload_unet()
-        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
-
-    def generate_images(
-        self,
-        prompts,
-        neg_prompts,
-        image,
-        batch_size,
-        height,
-        width,
-        num_inference_steps,
-        noise_level,
-        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 text embeddings with weight emphasis from prompts
-        text_embeddings = self.encode_prompts_weight(
-            prompts, neg_prompts, max_length
-        )
-
-        # 4. Preprocess image
-        image = preprocess(image).to(dtype)
-
-        # 5. Add noise to image
-        noise_level = torch.tensor([noise_level], dtype=torch.long)
-        noise = torch.randn(
-            image.shape,
-            generator=generator,
-        ).to(dtype)
-        image = self.low_res_scheduler.add_noise(image, noise, noise_level)
-        image = torch.cat([image] * 2)
-        noise_level = torch.cat([noise_level] * image.shape[0])
-
-        height, width = image.shape[2:]
-        # 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,
-        )
-
-        eta = 0.0
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 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,
-            image=image,
-            text_embeddings=text_embeddings,
-            guidance_scale=guidance_scale,
-            noise_level=noise_level,
-            total_timesteps=self.scheduler.timesteps,
-            dtype=dtype,
-            cpu_scheduling=cpu_scheduling,
-            extra_step_kwargs=extra_step_kwargs,
-        )
-
-        # Img latents -> PIL images
-        all_imgs = []
-        self.load_vae()
-        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)
-        if self.ondemand:
-            self.unload_vae()
-
-        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_utils.py

@@ -1,882 +0,0 @@
-import torch
-import numpy as np
-from transformers import CLIPTokenizer
-from PIL import Image
-from tqdm.auto import tqdm
-import time
-from typing import Union
-from diffusers import (
-    DDIMScheduler,
-    DDPMScheduler,
-    PNDMScheduler,
-    LMSDiscreteScheduler,
-    KDPM2DiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler,
-    DEISMultistepScheduler,
-)
-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,
-)
-import sys
-
-SD_STATE_IDLE = "idle"
-SD_STATE_CANCEL = "cancel"
-
-
-class StableDiffusionPipeline:
-    def __init__(
-        self,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            KDPM2DiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-            SharkEulerDiscreteScheduler,
-            DEISMultistepScheduler,
-        ],
-        sd_model: SharkifyStableDiffusionModel,
-        import_mlir: bool,
-        use_lora: str,
-        ondemand: bool,
-    ):
-        self.vae = None
-        self.text_encoder = None
-        self.unet = None
-        self.unet_512 = None
-        self.model_max_length = 77
-        self.scheduler = scheduler
-        # TODO: Implement using logging python utility.
-        self.log = ""
-        self.status = SD_STATE_IDLE
-        self.sd_model = sd_model
-        self.import_mlir = import_mlir
-        self.use_lora = use_lora
-        self.ondemand = ondemand
-        # TODO: Find a better workaround for fetching base_model_id early enough for CLIPTokenizer.
-        try:
-            self.tokenizer = get_tokenizer()
-        except:
-            self.load_unet()
-            self.unload_unet()
-            self.tokenizer = get_tokenizer()
-
-    def load_clip(self):
-        if self.text_encoder is not None:
-            return
-
-        if self.import_mlir or self.use_lora:
-            if not self.import_mlir:
-                print(
-                    "Warning: LoRA provided but import_mlir not specified. Importing MLIR anyways."
-                )
-            self.text_encoder = self.sd_model.clip()
-        else:
-            try:
-                self.text_encoder = get_clip()
-            except Exception as e:
-                print(e)
-                print("download pipeline failed, falling back to import_mlir")
-                self.text_encoder = self.sd_model.clip()
-
-    def unload_clip(self):
-        del self.text_encoder
-        self.text_encoder = None
-
-    def load_unet(self):
-        if self.unet is not None:
-            return
-
-        if self.import_mlir or self.use_lora:
-            self.unet = self.sd_model.unet()
-        else:
-            try:
-                self.unet = get_unet()
-            except Exception as e:
-                print(e)
-                print("download pipeline failed, falling back to import_mlir")
-                self.unet = self.sd_model.unet()
-
-    def unload_unet(self):
-        del self.unet
-        self.unet = None
-
-    def load_unet_512(self):
-        if self.unet_512 is not None:
-            return
-
-        if self.import_mlir or self.use_lora:
-            self.unet_512 = self.sd_model.unet(use_large=True)
-        else:
-            try:
-                self.unet_512 = get_unet(use_large=True)
-            except Exception as e:
-                print(e)
-                print("download pipeline failed, falling back to import_mlir")
-                self.unet_512 = self.sd_model.unet(use_large=True)
-
-    def unload_unet_512(self):
-        del self.unet_512
-        self.unet_512 = None
-
-    def load_vae(self):
-        if self.vae is not None:
-            return
-
-        if self.import_mlir or self.use_lora:
-            self.vae = self.sd_model.vae()
-        else:
-            try:
-                self.vae = get_vae()
-            except Exception as e:
-                print(e)
-                print("download pipeline failed, falling back to import_mlir")
-                self.vae = self.sd_model.vae()
-
-    def unload_vae(self):
-        del self.vae
-        self.vae = None
-
-    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])
-
-        self.load_clip()
-        clip_inf_start = time.time()
-        text_embeddings = self.text_encoder("forward", (text_input,))
-        clip_inf_time = (time.time() - clip_inf_start) * 1000
-        if self.ondemand:
-            self.unload_clip()
-        self.log += f"\nClip Inference time (ms) = {clip_inf_time:.3f}"
-
-        return text_embeddings
-
-    def decode_latents(self, latents, use_base_vae, cpu_scheduling):
-        if use_base_vae:
-            latents = 1 / 0.18215 * latents
-
-        latents_numpy = latents
-        if cpu_scheduling:
-            latents_numpy = latents.detach().numpy()
-
-        profile_device = start_profiling(file_path="vae.rdc")
-        vae_start = time.time()
-        images = self.vae("forward", (latents_numpy,))
-        vae_inf_time = (time.time() - vae_start) * 1000
-        end_profiling(profile_device)
-        self.log += f"\nVAE Inference time (ms): {vae_inf_time:.3f}"
-
-        if use_base_vae:
-            images = torch.from_numpy(images)
-            images = (images.detach().cpu() * 255.0).numpy()
-            images = images.round()
-
-        images = torch.from_numpy(images).to(torch.uint8).permute(0, 2, 3, 1)
-        pil_images = [Image.fromarray(image) for image in images.numpy()]
-        return pil_images
-
-    def produce_img_latents(
-        self,
-        latents,
-        text_embeddings,
-        guidance_scale,
-        total_timesteps,
-        dtype,
-        cpu_scheduling,
-        mask=None,
-        masked_image_latents=None,
-        return_all_latents=False,
-    ):
-        self.status = SD_STATE_IDLE
-        step_time_sum = 0
-        latent_history = [latents]
-        text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
-        text_embeddings_numpy = text_embeddings.detach().numpy()
-        if text_embeddings.shape[1] <= self.model_max_length:
-            self.load_unet()
-        else:
-            self.load_unet_512()
-        for i, t in tqdm(enumerate(total_timesteps)):
-            step_start_time = time.time()
-            timestep = torch.tensor([t]).to(dtype).detach().numpy()
-            latent_model_input = self.scheduler.scale_model_input(latents, t)
-            if mask is not None and masked_image_latents is not None:
-                latent_model_input = torch.cat(
-                    [
-                        torch.from_numpy(np.asarray(latent_model_input)),
-                        mask,
-                        masked_image_latents,
-                    ],
-                    dim=1,
-                ).to(dtype)
-            if cpu_scheduling:
-                latent_model_input = latent_model_input.detach().numpy()
-
-            # Profiling Unet.
-            profile_device = start_profiling(file_path="unet.rdc")
-            if text_embeddings.shape[1] <= self.model_max_length:
-                noise_pred = self.unet(
-                    "forward",
-                    (
-                        latent_model_input,
-                        timestep,
-                        text_embeddings_numpy,
-                        guidance_scale,
-                    ),
-                    send_to_host=False,
-                )
-            else:
-                noise_pred = self.unet_512(
-                    "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
-
-            if self.status == SD_STATE_CANCEL:
-                break
-
-        if self.ondemand:
-            self.unload_unet()
-            self.unload_unet_512()
-        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,
-            KDPM2DiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-            SharkEulerDiscreteScheduler,
-            DEISMultistepScheduler,
-        ],
-        import_mlir: bool,
-        model_id: str,
-        ckpt_loc: str,
-        custom_vae: str,
-        precision: str,
-        max_length: int,
-        batch_size: int,
-        height: int,
-        width: int,
-        use_base_vae: bool,
-        use_tuned: bool,
-        ondemand: bool,
-        low_cpu_mem_usage: bool = False,
-        debug: bool = False,
-        use_stencil: str = None,
-        use_lora: str = "",
-        ddpm_scheduler: DDPMScheduler = None,
-        use_quantize=None,
-    ):
-        if (
-            not import_mlir
-            and not use_lora
-            and cls.__name__ == "StencilPipeline"
-        ):
-            sys.exit("StencilPipeline not supported with SharkTank currently.")
-
-        is_inpaint = cls.__name__ in [
-            "InpaintPipeline",
-            "OutpaintPipeline",
-        ]
-        is_upscaler = cls.__name__ in ["UpscalerPipeline"]
-
-        sd_model = SharkifyStableDiffusionModel(
-            model_id,
-            ckpt_loc,
-            custom_vae,
-            precision,
-            max_len=max_length,
-            batch_size=batch_size,
-            height=height,
-            width=width,
-            use_base_vae=use_base_vae,
-            use_tuned=use_tuned,
-            low_cpu_mem_usage=low_cpu_mem_usage,
-            debug=debug,
-            is_inpaint=is_inpaint,
-            is_upscaler=is_upscaler,
-            use_stencil=use_stencil,
-            use_lora=use_lora,
-            use_quantize=use_quantize,
-        )
-
-        if cls.__name__ in ["UpscalerPipeline"]:
-            return cls(
-                scheduler,
-                ddpm_scheduler,
-                sd_model,
-                import_mlir,
-                use_lora,
-                ondemand,
-            )
-
-        return cls(scheduler, sd_model, import_mlir, use_lora, ondemand)
-
-    # #####################################################
-    # Implements text embeddings with weights from prompts
-    # https://huggingface.co/AlanB/lpw_stable_diffusion_mod
-    # #####################################################
-    def encode_prompts_weight(
-        self,
-        prompt,
-        negative_prompt,
-        model_max_length,
-        do_classifier_free_guidance=True,
-        max_embeddings_multiples=1,
-        num_images_per_prompt=1,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-        Args:
-            prompt (`str` or `list(int)`):
-                prompt to be encoded
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            model_max_length (int):
-                SHARK: pass the max length instead of relying on pipe.tokenizer.model_max_length
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not,
-                SHARK: must be set to True as we always expect neg embeddings (defaulted to True)
-            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
-                The max multiple length of prompt embeddings compared to the max output length of text encoder.
-                SHARK: max_embeddings_multiples>1 produce a tensor shape error (defaulted to 1)
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-                SHARK: num_images_per_prompt is not used (defaulted to 1)
-        """
-
-        # SHARK: Save model_max_length, load the clip and init inference time
-        self.model_max_length = model_max_length
-        self.load_clip()
-        clip_inf_start = time.time()
-
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        if negative_prompt is None:
-            negative_prompt = [""] * batch_size
-        elif isinstance(negative_prompt, str):
-            negative_prompt = [negative_prompt] * batch_size
-        if batch_size != len(negative_prompt):
-            raise ValueError(
-                f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                " the batch size of `prompt`."
-            )
-
-        text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
-            pipe=self,
-            prompt=prompt,
-            uncond_prompt=negative_prompt
-            if do_classifier_free_guidance
-            else None,
-            max_embeddings_multiples=max_embeddings_multiples,
-        )
-        # SHARK: we are not using num_images_per_prompt
-        # bs_embed, seq_len, _ = text_embeddings.shape
-        # text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
-        # text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        if do_classifier_free_guidance:
-            # SHARK: we are not using num_images_per_prompt
-            # bs_embed, seq_len, _ = uncond_embeddings.shape
-            # uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
-            # uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        if text_embeddings.shape[1] > model_max_length:
-            pad = (0, 0) * (len(text_embeddings.shape) - 2)
-            pad = pad + (0, 512 - text_embeddings.shape[1])
-            text_embeddings = torch.nn.functional.pad(text_embeddings, pad)
-
-        # SHARK: Report clip inference time
-        clip_inf_time = (time.time() - clip_inf_start) * 1000
-        if self.ondemand:
-            self.unload_clip()
-        self.log += f"\nClip Inference time (ms) = {clip_inf_time:.3f}"
-
-        return text_embeddings.numpy()
-
-
-from typing import List, Optional, Union
-import re
-
-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: StableDiffusionPipeline, 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: StableDiffusionPipeline,
-    text_input: torch.Tensor,
-    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.text_encoder(text_input_chunk)[0]
-            # SHARK: deplicate the text_input as Shark runner expects tokens and neg tokens
-            formatted_text_input_chunk = torch.cat(
-                [text_input_chunk, text_input_chunk]
-            )
-            text_embedding = pipe.text_encoder(
-                "forward", (formatted_text_input_chunk,)
-            )[0]
-
-            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)[None, :]
-    else:
-        # SHARK: deplicate the text_input as Shark runner expects tokens and neg tokens
-        # Convert the result to tensor
-        # text_embeddings = pipe.text_encoder(text_input)[0]
-        formatted_text_input = torch.cat([text_input, text_input])
-        text_embeddings = pipe.text_encoder(
-            "forward", (formatted_text_input,)
-        )[0]
-        text_embeddings = torch.from_numpy(text_embeddings)[None, :]
-    return text_embeddings
-
-
-def get_weighted_text_embeddings(
-    pipe: StableDiffusionPipeline,
-    prompt: Union[str, List[str]],
-    uncond_prompt: Optional[Union[str, List[str]]] = None,
-    max_embeddings_multiples: Optional[int] = 3,
-    no_boseos_middle: Optional[bool] = False,
-    skip_parsing: Optional[bool] = False,
-    skip_weighting: Optional[bool] = False,
-):
-    r"""
-    Prompts can be assigned with local weights using brackets. For example,
-    prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
-    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
-    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
-    Args:
-        pipe (`StableDiffusionPipeline`):
-            Pipe to provide access to the tokenizer and the text encoder.
-        prompt (`str` or `List[str]`):
-            The prompt or prompts to guide the image generation.
-        uncond_prompt (`str` or `List[str]`):
-            The unconditional prompt or prompts for guide the image generation. If unconditional prompt
-            is provided, the embeddings of prompt and uncond_prompt are concatenated.
-        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
-            The max multiple length of prompt embeddings compared to the max output length of text encoder.
-        no_boseos_middle (`bool`, *optional*, defaults to `False`):
-            If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
-            ending token in each of the chunk in the middle.
-        skip_parsing (`bool`, *optional*, defaults to `False`):
-            Skip the parsing of brackets.
-        skip_weighting (`bool`, *optional*, defaults to `False`):
-            Skip the weighting. When the parsing is skipped, it is forced True.
-    """
-    max_length = (pipe.model_max_length - 2) * max_embeddings_multiples + 2
-    if isinstance(prompt, str):
-        prompt = [prompt]
-
-    if not skip_parsing:
-        prompt_tokens, prompt_weights = get_prompts_with_weights(
-            pipe, prompt, max_length - 2
-        )
-        if uncond_prompt is not None:
-            if isinstance(uncond_prompt, str):
-                uncond_prompt = [uncond_prompt]
-            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,
-    )
-    # 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,
-        )
-        # 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/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

@@ -1,66 +0,0 @@
-from diffusers import (
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    DDPMScheduler,
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    KDPM2DiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    DEISMultistepScheduler,
-)
-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["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[
-        "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["DEISMultistep"] = DEISMultistepScheduler.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

@@ -1,157 +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
-        device = args.device.split(":", 1)[0].strip()
-
-        model_input = {
-            "euler": {
-                "latent": torch.randn(
-                    BATCH_SIZE, 4, args.height // 8, args.width // 8
-                ),
-                "output": torch.randn(
-                    BATCH_SIZE, 4, args.height // 8, args.width // 8
-                ),
-                "sigma": torch.tensor(1).to(torch.float32),
-                "dt": torch.tensor(1).to(torch.float32),
-            },
-        }
-
-        example_latent = model_input["euler"]["latent"]
-        example_output = model_input["euler"]["output"]
-        if args.precision == "fp16":
-            example_latent = example_latent.half()
-            example_output = example_output.half()
-        example_sigma = model_input["euler"]["sigma"]
-        example_dt = model_input["euler"]["dt"]
-
-        class ScalingModel(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-
-            def forward(self, latent, sigma):
-                return latent / ((sigma**2 + 1) ** 0.5)
-
-        class SchedulerStepModel(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-
-            def forward(self, noise_pred, sigma, latent, dt):
-                pred_original_sample = latent - sigma * noise_pred
-                derivative = (latent - pred_original_sample) / sigma
-                return latent + derivative * dt
-
-        iree_flags = []
-        if len(args.iree_vulkan_target_triple) > 0:
-            iree_flags.append(
-                f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
-            )
-        # Disable bindings fusion to work with moltenVK.
-        if sys.platform == "darwin":
-            iree_flags.append("-iree-stream-fuse-binding=false")
-
-        def _import(self):
-            scaling_model = ScalingModel()
-            self.scaling_model, _ = compile_through_fx(
-                model=scaling_model,
-                inputs=(example_latent, example_sigma),
-                extended_model_name=f"euler_scale_model_input_{BATCH_SIZE}_{args.height}_{args.width}_{device}_"
-                + 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),
-                extended_model_name=f"euler_step_{BATCH_SIZE}_{args.height}_{args.width}_{device}_"
-                + args.precision,
-                extra_args=iree_flags,
-            )
-
-        if args.import_mlir:
-            _import(self)
-
-        else:
-            try:
-                self.scaling_model = get_shark_model(
-                    SCHEDULER_BUCKET,
-                    "euler_scale_model_input_" + args.precision,
-                    iree_flags,
-                )
-                self.step_model = get_shark_model(
-                    SCHEDULER_BUCKET,
-                    "euler_step_" + args.precision,
-                    iree_flags,
-                )
-            except:
-                print(
-                    "failed to download model, falling back and using import_mlir"
-                )
-                args.import_mlir = True
-                _import(self)
-
-    def scale_model_input(self, sample, timestep):
-        step_index = (self.timesteps == timestep).nonzero().item()
-        sigma = self.sigmas[step_index]
-        return self.scaling_model(
-            "forward",
-            (
-                sample,
-                sigma,
-            ),
-            send_to_host=False,
-        )
-
-    def step(self, noise_pred, timestep, latent):
-        step_index = (self.timesteps == timestep).nonzero().item()
-        sigma = self.sigmas[step_index]
-        dt = self.sigmas[step_index + 1] - sigma
-        return self.step_model(
-            "forward",
-            (
-                noise_pred,
-                sigma,
-                latent,
-                dt,
-            ),
-            send_to_host=False,
-        )

apps/stable_diffusion/src/utils/__init__.py

@@ -1,41 +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.stencils.stencil_utils import (
-    controlnet_hint_conversion,
-    get_stencil_model_id,
-)
-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,
-    convert_original_vae,
-    fetch_and_update_base_model_id,
-    get_path_to_diffusers_checkpoint,
-    sanitize_seed,
-    get_path_stem,
-    get_extended_name,
-    get_generated_imgs_path,
-    get_generated_imgs_todays_subdir,
-    clear_all,
-    save_output_img,
-    get_generation_text_info,
-    update_lora_weight,
-    resize_stencil,
-    _compile_module,
-)

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

@@ -1,296 +0,0 @@
-{
-    "clip": {
-        "token" : {
-            "shape" : [
-                "2*batch_size",
-                "max_len"
-            ],
-            "dtype":"i64"
-        }
-    },
-    "vae_encode": {
-        "image" : {
-            "shape" : [
-                "1*batch_size",3,"8*height","8*width"
-            ],
-            "dtype":"f32"
-        }
-    },
-    "vae": {
-        "vae": {
-            "latents" : {
-                "shape" : [
-                    "1*batch_size",4,"height","width"
-                ],
-                "dtype":"f32"
-            }
-        },
-        "vae_upscaler": {
-            "latents" : {
-                "shape" : [
-                    "1*batch_size",4,"8*height","8*width"
-                ],
-                "dtype":"f32"
-            }
-        }
-    },
-    "unet": {
-        "stabilityai/stable-diffusion-2-1": {
-            "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"
-            }
-        },
-        "CompVis/stable-diffusion-v1-4": {
-            "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"
-            }
-        },
-        "stabilityai/stable-diffusion-2-inpainting": {
-            "latents": {
-                "shape": [
-                    "1*batch_size",
-                    9,
-                    "height",
-                    "width"
-                ],
-                "dtype": "f32"
-            },
-            "timesteps": {
-                "shape": [
-                    1
-                ],
-                "dtype": "f32"
-            },
-            "embedding": {
-                "shape": [
-                    "2*batch_size",
-                    "max_len",
-                    1024
-                ],
-                "dtype": "f32"
-            },
-            "guidance_scale": {
-                "shape": 2,
-                "dtype": "f32"
-            }
-        },
-        "runwayml/stable-diffusion-inpainting": {
-            "latents": {
-                "shape": [
-                    "1*batch_size",
-                    9,
-                    "height",
-                    "width"
-                ],
-                "dtype": "f32"
-            },
-            "timesteps": {
-                "shape": [
-                    1
-                ],
-                "dtype": "f32"
-            },
-            "embedding": {
-                "shape": [
-                    "2*batch_size",
-                    "max_len",
-                    768
-                ],
-                "dtype": "f32"
-            },
-            "guidance_scale": {
-                "shape": 2,
-                "dtype": "f32"
-            }
-        },
-        "stabilityai/stable-diffusion-x4-upscaler": {
-            "latents": {
-                "shape": [
-                    "2*batch_size",
-                    7,
-                    "8*height",
-                    "8*width"
-                ],
-                "dtype": "f32"
-            },
-            "timesteps": {
-                "shape": [
-                    1
-                ],
-                "dtype": "f32"
-            },
-            "embedding": {
-                "shape": [
-                    "2*batch_size",
-                    "max_len",
-                    1024
-                ],
-                "dtype": "f32"
-            },
-            "noise_level": {
-                "shape": [2],
-                "dtype": "i64"
-            }
-        }
-    },
-    "stencil_adaptor": {
-        "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"
-        },
-        "controlnet_hint": {
-            "shape": [1, 3, "8*height", "8*width"],
-            "dtype": "f32"
-        }
-    },
-    "stencil_unet": {
-        "CompVis/stable-diffusion-v1-4": {
-            "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"
-            },
-            "control1": {
-                "shape": [2, 320, "height", "width"],
-                "dtype": "f32"
-            },
-            "control2": {
-                "shape": [2, 320, "height", "width"],
-                "dtype": "f32"
-            },
-            "control3": {
-                "shape": [2, 320, "height", "width"],
-                "dtype": "f32"
-            },
-            "control4": {
-                "shape": [2, 320, "height/2", "width/2"],
-                "dtype": "f32"
-            },
-            "control5": {
-                "shape": [2, 640, "height/2", "width/2"],
-                "dtype": "f32"
-            },
-            "control6": {
-                "shape": [2, 640, "height/2", "width/2"],
-                "dtype": "f32"
-            },
-            "control7": {
-                "shape": [2, 640, "height/4", "width/4"],
-                "dtype": "f32"
-            },
-            "control8": {
-                "shape": [2, 1280, "height/4", "width/4"],
-                "dtype": "f32"
-            },
-            "control9": {
-                "shape": [2, 1280, "height/4", "width/4"],
-                "dtype": "f32"
-            },
-            "control10": {
-                "shape": [2, 1280, "height/8", "width/8"],
-                "dtype": "f32"
-            },
-            "control11": {
-                "shape": [2, 1280, "height/8", "width/8"],
-                "dtype": "f32"
-            },
-            "control12": {
-                "shape": [2, 1280, "height/8", "width/8"],
-                "dtype": "f32"
-            },
-            "control13": {
-                "shape": [2, 1280, "height/8", "width/8"],
-                "dtype": "f32"
-            }
-        }
-    }
-}

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

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

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

@@ -1,19 +0,0 @@
-[
-  {
-    "stablediffusion/untuned":"gs://shark_tank/nightly"
-  },
-  {
-    "stablediffusion/v1_4/unet/fp16/length_64/untuned":"unet_1_64_512_512_fp16_stable-diffusion-v1-4_vulkan",
-    "stablediffusion/v1_4/vae/fp16/length_77/untuned":"vae_1_64_512_512_fp16_stable-diffusion-v1-4_vulkan",
-    "stablediffusion/v1_4/vae/fp16/length_64/untuned":"vae_1_64_512_512_fp16_stable-diffusion-v1-4_vulkan",
-    "stablediffusion/v1_4/clip/fp32/length_64/untuned":"clip_1_64_512_512_fp16_stable-diffusion-v1-4_vulkan",
-    "stablediffusion/v2_1base/unet/fp16/length_77/untuned":"unet_1_77_512_512_fp16_stable-diffusion-2-1-base_vulkan",
-    "stablediffusion/v2_1base/unet/fp16/length_64/untuned":"unet_1_64_512_512_fp16_stable-diffusion-2-1-base_vulkan",
-    "stablediffusion/v2_1base/vae/fp16/length_77/untuned":"vae_1_64_512_512_fp16_stable-diffusion-2-1-base_vulkan",
-    "stablediffusion/v2_1base/clip/fp32/length_77/untuned":"clip_1_77_512_512_fp16_stable-diffusion-2-1-base_vulkan",
-    "stablediffusion/v2_1base/clip/fp32/length_64/untuned":"clip_1_64_512_512_fp16_stable-diffusion-2-1-base_vulkan",
-    "stablediffusion/v2_1/unet/fp16/length_77/untuned":"unet_1_77_512_512_fp16_stable-diffusion-2-1-base_vulkan",
-    "stablediffusion/v2_1/vae/fp16/length_77/untuned":"vae_1_64_512_512_fp16_stable-diffusion-2-1-base_vulkan",
-    "stablediffusion/v2_1/clip/fp32/length_77/untuned":"clip_1_64_512_512_fp16_stable-diffusion-2-1-base_vulkan"
-  }
-]