Disable tuned configs on all inpainting models (#1086)

* Add DEISMultistep scheduler #1076

* line lenght lint fix

.github/workflows/nightly.yml

@@ -10,14 +10,14 @@ on:
 
 jobs:
   windows-build:
-    runs-on: windows-latest
+    runs-on: 7950X
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v3
       with:
@@ -44,14 +44,20 @@ jobs:
         body: |
           Automatic snapshot release of nod.ai SHARK.
         draft: true
-        prerelease: false
+        prerelease: true
 
     - name: Build Package 
       shell: powershell
       run: |
         ./setup_venv.ps1
-        pyinstaller web/shark_sd.spec
+        python process_skipfiles.py
+        pyinstaller .\apps\stable_diffusion\shark_sd.spec
         mv ./dist/shark_sd.exe ./dist/shark_sd_${{ env.package_version_ }}.exe
+        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_${{ env.package_version_ }}.exe
+        pyinstaller .\apps\stable_diffusion\shark_sd_cli.spec
+        python process_skipfiles.py
+        mv ./dist/shark_sd_cli.exe ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
+        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
 
         
     # GHA windows VM OOMs so disable for now
@@ -61,9 +67,9 @@ jobs:
     #    $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
     #    pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
 
-    - uses: actions/upload-artifact@v2
-      with:
-        path: dist/*
+    #- uses: actions/upload-artifact@v2
+    #  with:
+    #    path: dist/*
     
     - name: Upload Release Assets
       id: upload-release-assets
@@ -73,6 +79,7 @@ jobs:
       with:
         release_id: ${{ steps.create_release.outputs.id }}
         assets_path: ./dist/*
+        #asset_content_type: application/vnd.microsoft.portable-executable 
 
     - name: Publish Release
       id: publish_release
@@ -88,7 +95,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
         backend: [IREE, SHARK]
 
     steps:
@@ -127,7 +134,7 @@ jobs:
         source iree.venv/bin/activate
         package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
         SHARK_PACKAGE_VERSION=${package_version} \
-        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://iree-org.github.io/iree/pip-release-links.html
+        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://openxla.github.io/iree/pip-release-links.html
         # Install the built wheel
         pip install ./wheelhouse/nodai*
         # Validate the Models
@@ -139,7 +146,7 @@ jobs:
           then 
             export SHA=$(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}_$SHA/* gs://shark_tank/latest/
+            gsutil -m cp -r gs://shark_tank/${DATE}_$SHA/* gs://shark_tank/nightly/
         fi
         rm -rf ./wheelhouse/nodai*
 

.github/workflows/test-models.yml

@@ -29,9 +29,9 @@ jobs:
     strategy:
       fail-fast: true
       matrix:
-        os: [icelake, a100, MacStudio, ubuntu-latest]
+        os: [7950x, icelake, a100, MacStudio, ubuntu-latest]
         suite: [cpu,cuda,vulkan]
-        python-version: ["3.10"]
+        python-version: ["3.11"]
         include:
           - os: ubuntu-latest
             suite: lint
@@ -52,13 +52,19 @@ jobs:
             suite: cuda
           - os: a100
             suite: cpu
+          - os: 7950x
+            suite: cpu
+          - os: 7950x
+            suite: cuda
 
     runs-on: ${{ matrix.os }}
 
     steps:
     - uses: actions/checkout@v3
+      if: matrix.os != '7950x'
     
     - name: Set Environment Variables
+      if: matrix.os != '7950x'
       run: |
         echo "SHORT_SHA=`git rev-parse --short=4 HEAD`" >> $GITHUB_ENV
         echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
@@ -78,6 +84,9 @@ jobs:
         #cache-dependency-path: |
         #  **/requirements-importer.txt
         #  **/requirements.txt
+    
+    - uses: actions/checkout@v2
+      if: matrix.os == '7950x'
           
     - name: Install dependencies
       if: matrix.suite == 'lint'
@@ -100,9 +109,9 @@ jobs:
       if: matrix.suite == 'cpu'
       run: |
         cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
+        PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest --benchmark --ci --ci_sha=${SHORT_SHA} -s --local_tank_cache="/data/anush/shark_cache" tank/test_models.py -k cpu --update_tank
+        pytest --forked --benchmark --ci --ci_sha=${SHORT_SHA} --update_tank -k cpu
         gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv
         gsutil cp gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv gs://shark-public/builder/bench_results/latest/bench_results_cpu_latest.csv
 
@@ -112,25 +121,41 @@ jobs:
         cd $GITHUB_WORKSPACE
         PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest --benchmark --ci --ci_sha=${SHORT_SHA} -s --local_tank_cache="/data/anush/shark_cache" tank/test_models.py -k cuda --update_tank
+        pytest --forked --benchmark --ci --ci_sha=${SHORT_SHA} --update_tank -k cuda
         gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${DATE}/bench_results_cuda_${SHORT_SHA}.csv
         gsutil cp gs://shark-public/builder/bench_results/${DATE}/bench_results_cuda_${SHORT_SHA}.csv gs://shark-public/builder/bench_results/latest/bench_results_cuda_latest.csv
+        # Disabled due to black image bug
+        # python build_tools/stable_diffusion_testing.py --device=cuda 
 
     - name: Validate Vulkan Models (MacOS)
       if: matrix.suite == 'vulkan' && matrix.os == 'MacStudio'
       run: |
         cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
+        PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
         source shark.venv/bin/activate
         export DYLD_LIBRARY_PATH=/usr/local/lib/
         echo $PATH
         pip list | grep -E "torch|iree"
-        pytest -s --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/Volumes/builder/anush/shark_cache" tank/test_models.py -k vulkan --update_tank
+        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/Volumes/builder/anush/shark_cache" -k vulkan --update_tank
 
     - name: Validate Vulkan Models (a100)
-      if: matrix.suite == 'vulkan' && matrix.os != 'MacStudio'
+      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 --benchmark --ci --ci_sha=${SHORT_SHA} -s --local_tank_cache="/data/anush/shark_cache" tank/test_models.py -k vulkan --update_tank
+        pytest --forked --benchmark --ci --ci_sha=${SHORT_SHA} --update_tank -k vulkan
+        python build_tools/stable_diffusion_testing.py --device=vulkan
+
+    - name: Validate Vulkan Models (Windows)
+      if: matrix.suite == 'vulkan' && matrix.os == '7950x'
+      run: |
+        ./setup_venv.ps1
+        pytest --benchmark -k vulkan -s
+        type bench_results.csv
+
+    - name: Validate Stable Diffusion Models (Windows)
+      if: matrix.suite == 'vulkan' && matrix.os == '7950x'
+      run: |
+        ./setup_venv.ps1
+        python build_tools/stable_diffusion_testing.py --device=vulkan

.gitignore

@@ -159,6 +159,9 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+# vscode related
+.vscode
+
 # Shark related artefacts
 *venv/
 shark_tmp/
@@ -170,6 +173,12 @@ tank/dict_configs.py
 cache_models/
 onnx_models/
 
-#web logging
-web/logs/
-web/stored_results/stable_diffusion/
+# Generated images
+generated_imgs/
+
+# Custom model related artefacts
+variants.json
+models/
+
+# models folder
+apps/stable_diffusion/web/models/

README.md

@@ -1,12 +1,61 @@
 # SHARK
 
-High Performance Machine Learning and Data Analytics for CPUs, GPUs, Accelerators and Heterogeneous Clusters
+High Performance Machine Learning Distribution
 
 [![Nightly Release](https://github.com/nod-ai/SHARK/actions/workflows/nightly.yml/badge.svg)](https://github.com/nod-ai/SHARK/actions/workflows/nightly.yml)
 [![Validate torch-models on Shark Runtime](https://github.com/nod-ai/SHARK/actions/workflows/test-models.yml/badge.svg)](https://github.com/nod-ai/SHARK/actions/workflows/test-models.yml)
 
 
-## Installation (Windows, Linux and macOS)
+<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. 
+* [Nvidia Users] Download and install the latest CUDA / Vulkan drivers from [here](https://developer.nvidia.com/cuda-downloads)
+  
+#### Linux Drivers
+* MESA / RADV drivers wont work with FP16. Please use the latest AMGPU-PRO drivers (non-pro OSS drivers also wont work) or the latest NVidia Linux Drivers.
+
+Other users please ensure you have your latest vendor drivers and Vulkan SDK from [here](https://vulkan.lunarg.com/sdk/home) and if you are using vulkan check `vulkaninfo` works in a terminal window
+
+</details>
+
+
+ 
+### Quick Start for SHARK Stable Diffusion for Windows 10/11 Users
+
+Install 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)
+
+Double click the .exe 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. 
+
+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` 
+
+## Running
+
+* Open a Command Prompt or Powershell terminal, change folder (`cd`) to the .exe folder. Then run the EXE from the command prompt. That way, if an error occurs, you'll be able to cut-and-paste it to ask for help. (if it always works for you without error, you may simply double-click the EXE)
+* The first run may take few minutes when the models are downloaded and compiled. Your patience is appreciated. The download could be about 5GB.
+* You will likely see a Windows Defender message asking you to give permission to open a web server port. Accept it.
+* Open a browser to access the Stable Diffusion web server. By default, the port is 8080, so you can go to http://localhost:8080/.
+
+## Stopping
+
+* Select the command prompt that's running the EXE. Press CTRL-C and wait a moment or close the terminal. 
+</details>
+
+<details>
+  <summary>Advanced Installation (Only for developers)</summary>
+  
+## Advanced Installation (Windows, Linux and macOS) for developers
 
 ## Check out the code
 
@@ -19,7 +68,7 @@ cd SHARK
 
 ### Windows 10/11 Users
 
-* Install the latest Python 3.10.x version from [here](https://www.python.org/downloads/windows/)
+* 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)
 
@@ -45,12 +94,12 @@ source shark.venv/bin/activate
 
 #### Windows 10/11 Users
 ```powershell
-(shark.venv) PS C:\Users\nod\SHARK> cd web
-(shark.venv) PS C:\Users\nod\SHARK\web> python index.py
+(shark.venv) PS C:\g\shark> cd .\apps\stable_diffusion\web\
+(shark.venv) PS C:\g\shark\apps\stable_diffusion\web> python .\index.py
 ```
-#### Linux Users
+#### Linux / macOS Users
 ```shell
-(shark.venv) > cd web
+(shark.venv) > cd apps/stable_diffusion/web
 (shark.venv) > python index.py
 ```
 
@@ -63,39 +112,27 @@ source shark.venv/bin/activate
 
 ### Run Stable Diffusion on your device - Commandline
 
-#### Install your hardware drivers
-* [AMD RDNA Users] Download the latest driver [here](https://www.amd.com/en/support/kb/release-notes/rn-rad-win-22-11-1-mril-iree)
-* [macOS Users] Download and install the latest Vulkan SDK from [here](https://vulkan.lunarg.com/sdk/home)
-* [Nvidia Users] Download and install the latest CUDA / Vulkan drivers from [here](https://developer.nvidia.com/cuda-downloads)
-
-Other users please ensure you have your latest vendor drivers and Vulkan SDK from [here](https://vulkan.lunarg.com/sdk/home) and if you are using vulkan check `vulkaninfo` works in a terminal window
-
-
 #### Windows 10/11 Users
 ```powershell
-(shark.venv) PS C:\g\shark> python .\shark\examples\shark_inference\stable_diffusion\main.py --precision="fp16" --prompt="tajmahal, snow, sunflowers, oil on canvas" --device="vulkan"
+(shark.venv) PS C:\g\shark> python .\apps\stable_diffusion\scripts\txt2img.py --precision="fp16" --prompt="tajmahal, snow, sunflowers, oil on canvas" --device="vulkan"
 ```
 
 #### Linux / macOS Users
 ```shell
-python3.10 shark/examples/shark_inference/stable_diffusion/main.py --precision=fp16 --device=vulkan --prompt="tajmahal, oil on canvas, sunflowers, 4k, uhd"
+python3.11 apps/stable_diffusion/scripts/txt2img.py --precision=fp16 --device=vulkan --prompt="tajmahal, oil on canvas, sunflowers, 4k, uhd"
 ```
 
 You can replace `vulkan` with `cpu` to run on your CPU or with `cuda` to run on CUDA devices. If you have multiple vulkan devices you can address them with `--device=vulkan://1` etc
+</details>
 
-The output on a 6900XT would like:
+The output on a AMD 7900XTX would look something like:
 
-```shell 
-44it [00:08,  5.14it/s]i = 44 t = 120 (191ms)
-45it [00:08,  5.15it/s]i = 45 t = 100 (191ms)
-46it [00:08,  5.16it/s]i = 46 t = 80 (191ms)
-47it [00:09,  5.16it/s]i = 47 t = 60 (193ms)
-48it [00:09,  5.15it/s]i = 48 t = 40 (195ms)
-49it [00:09,  5.12it/s]i = 49 t = 20 (196ms)
-50it [00:09,  5.14it/s]
-Average step time: 192.8154182434082ms/it
-Total image generation runtime (s): 10.390909433364868
-(shark.venv) PS C:\g\shark>
+```shell
+Average step time: 47.19188690185547ms/it
+Clip Inference time (ms) = 109.531
+VAE Inference time (ms): 78.590
+
+Total image generation time: 2.5788655281066895sec
 ```
 
 Here are some samples generated:
@@ -105,9 +142,6 @@ 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)
 
 
-
-For more options to the Stable Diffusion model read [this](https://github.com/nod-ai/SHARK/blob/main/shark/examples/shark_inference/stable_diffusion/README.md)
-
 Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any trouble with running it on your hardware. 
 
 
@@ -119,7 +153,7 @@ Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any
 This step sets up a new VirtualEnv for Python
 
 ```shell
-python --version #Check you have 3.10 on Linux, macOS or Windows Powershell
+python --version #Check you have 3.11 on Linux, macOS or Windows Powershell
 python -m venv shark_venv
 source shark_venv/bin/activate   # Use shark_venv/Scripts/activate on Windows
 
@@ -133,7 +167,7 @@ python -m pip install --upgrade pip
 
 ### Install SHARK
 
-This step pip installs SHARK and related packages on Linux Python 3.7, 3.8, 3.9, 3.10 and macOS Python 3.10
+This step pip installs SHARK and related packages on Linux Python 3.8, 3.10 and 3.11 and macOS / Windows Python 3.11
 
 ```shell
 pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f  https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
@@ -168,10 +202,10 @@ python ./minilm_jit.py --device="cpu"  #use cuda or vulkan or metal
 <details>
   <summary>Development, Testing and Benchmarks</summary>
 
-If you want to use Python3.10 and with TF Import tools you can use the environment variables like:
+If you want to use Python3.11 and with TF Import tools you can use the environment variables like:
 Set `USE_IREE=1` to use upstream IREE
 ```
-# PYTHON=python3.10 VENV_DIR=0617_venv IMPORTER=1 ./setup_venv.sh 
+# PYTHON=python3.11 VENV_DIR=0617_venv IMPORTER=1 ./setup_venv.sh 
 ```
 
 ### Run any of the hundreds of SHARK tank models via the test framework
@@ -181,14 +215,14 @@ python -m  shark.examples.shark_inference.resnet50_script --device="cpu" # Use g
 pytest tank/test_models.py -k "MiniLM"
 ```
   
-
+### How to use your locally built IREE / Torch-MLIR with SHARK
 If you are a *Torch-mlir developer or an IREE developer* and want to test local changes you can uninstall
 the provided packages with `pip uninstall torch-mlir` and / or `pip uninstall iree-compiler iree-runtime` and build locally
 with Python bindings and set your PYTHONPATH as mentioned [here](https://github.com/iree-org/iree/tree/main/docs/api_docs/python#install-iree-binaries)
 for IREE and [here](https://github.com/llvm/torch-mlir/blob/main/development.md#setup-python-environment-to-export-the-built-python-packages)
 for Torch-MLIR.
 
-### How to use your locally built Torch-MLIR with SHARK
+How to use your locally built Torch-MLIR with SHARK:
 ```shell
 1.) Run `./setup_venv.sh in SHARK` and activate `shark.venv` virtual env.
 2.) Run `pip uninstall torch-mlir`.
@@ -206,9 +240,15 @@ Now the SHARK will use your locally build Torch-MLIR repo.
 
 ## Benchmarking Dispatches
 
-To produce benchmarks of individual dispatches, you can add `--dispatch_benchmarks=All --dispatch_benchmarks_dir=<output_dir>` to your command line argument.  
+To produce benchmarks of individual dispatches, you can add `--dispatch_benchmarks=All --dispatch_benchmarks_dir=<output_dir>` to your pytest command line argument.  
 If you only want to compile specific dispatches, you can specify them with a space seperated string instead of `"All"`.  E.G. `--dispatch_benchmarks="0 1 2 10"`
 
+For example, to generate and run dispatch benchmarks for MiniLM on CUDA:
+```
+pytest -k "MiniLM and torch and static and cuda" --benchmark_dispatches=All -s --dispatch_benchmarks_dir=./my_dispatch_benchmarks                                                                                
+```
+The given command will populate `<dispatch_benchmarks_dir>/<model_name>/` with an `ordered_dispatches.txt` that lists and orders the dispatches and their latencies, as well as folders for each dispatch that contain .mlir, .vmfb, and results of the benchmark for that dispatch.
+
 if you want to instead incorporate this into a python script, you can pass the `dispatch_benchmarks` and `dispatch_benchmarks_dir` commands when initializing `SharkInference`, and the benchmarks will be generated when compiled.  E.G:
 
 ```
@@ -232,7 +272,7 @@ Output will include:
 - A .txt file containing benchmark output
 
 
-See tank/README.md for instructions on how to run model tests and benchmarks from the SHARK tank.
+See tank/README.md for further instructions on how to run model tests and benchmarks from the SHARK tank.
 
 </details>
 

apps/stable_diffusion/profiling_with_iree.md

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

apps/stable_diffusion/scripts/__init__.py

@@ -0,0 +1,4 @@
+from apps.stable_diffusion.scripts.txt2img import txt2img_inf
+from apps.stable_diffusion.scripts.img2img import img2img_inf
+from apps.stable_diffusion.scripts.inpaint import inpaint_inf
+from apps.stable_diffusion.scripts.outpaint import outpaint_inf

apps/stable_diffusion/scripts/img2img.py

@@ -0,0 +1,264 @@
+import sys
+import torch
+import time
+from PIL import Image
+from dataclasses import dataclass
+from apps.stable_diffusion.src import (
+    args,
+    Image2ImagePipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+
+
+@dataclass
+class Config:
+    model_id: str
+    ckpt_loc: str
+    precision: str
+    batch_size: int
+    max_length: int
+    height: int
+    width: int
+    device: str
+
+
+img2img_obj = None
+config_obj = None
+schedulers = None
+
+
+# Exposed to UI.
+def img2img_inf(
+    prompt: str,
+    negative_prompt: str,
+    init_image: str,
+    height: int,
+    width: int,
+    steps: int,
+    strength: float,
+    guidance_scale: float,
+    seed: int,
+    batch_count: int,
+    batch_size: int,
+    scheduler: str,
+    custom_model: str,
+    hf_model_id: str,
+    precision: str,
+    device: str,
+    max_length: int,
+    save_metadata_to_json: bool,
+    save_metadata_to_png: bool,
+):
+    global img2img_obj
+    global config_obj
+    global schedulers
+
+    args.prompts = [prompt]
+    args.negative_prompts = [negative_prompt]
+    args.guidance_scale = guidance_scale
+    args.seed = seed
+    args.steps = steps
+    args.strength = strength
+    args.scheduler = scheduler
+    args.img_path = init_image
+    image = Image.open(args.img_path).convert("RGB")
+
+    # set ckpt_loc and hf_model_id.
+    types = (
+        ".ckpt",
+        ".safetensors",
+    )  # the tuple of file types
+    args.ckpt_loc = ""
+    args.hf_model_id = ""
+    if custom_model == "None":
+        if not hf_model_id:
+            return (
+                None,
+                "Please provide either custom model or huggingface model ID, both must not be empty",
+            )
+        args.hf_model_id = hf_model_id
+    elif ".ckpt" in custom_model or ".safetensors" in custom_model:
+        args.ckpt_loc = custom_model
+    else:
+        args.hf_model_id = custom_model
+
+    if image is None:
+        return None, "An Initial Image is required"
+
+    args.save_metadata_to_json = save_metadata_to_json
+    args.write_metadata_to_png = save_metadata_to_png
+
+    dtype = torch.float32 if precision == "fp32" else torch.half
+    cpu_scheduling = not scheduler.startswith("Shark")
+    new_config_obj = Config(
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        height,
+        width,
+        device,
+    )
+    if not img2img_obj or config_obj != new_config_obj:
+        config_obj = new_config_obj
+        args.precision = precision
+        args.batch_size = batch_size
+        args.max_length = max_length
+        args.height = height
+        args.width = width
+        args.device = device.split("=>", 1)[1].strip()
+        args.iree_vulkan_target_triple = ""
+        args.use_tuned = True
+        args.import_mlir = True
+        set_init_device_flags()
+        model_id = (
+            args.hf_model_id
+            if args.hf_model_id
+            else "runwayml/stable-diffusion-inpainting"
+        )
+        schedulers = get_schedulers(model_id)
+        scheduler_obj = schedulers[scheduler]
+        img2img_obj = Image2ImagePipeline.from_pretrained(
+            scheduler_obj,
+            args.import_mlir,
+            args.hf_model_id,
+            args.ckpt_loc,
+            args.custom_vae,
+            args.precision,
+            args.max_length,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.use_base_vae,
+            args.use_tuned,
+            low_cpu_mem_usage=args.low_cpu_mem_usage,
+        )
+
+    img2img_obj.scheduler = schedulers[scheduler]
+
+    start_time = time.time()
+    img2img_obj.log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+    for current_batch in range(batch_count):
+        if current_batch > 0:
+            img_seed = utils.sanitize_seed(-1)
+        out_imgs = img2img_obj.generate_images(
+            prompt,
+            negative_prompt,
+            image,
+            batch_size,
+            height,
+            width,
+            steps,
+            strength,
+            guidance_scale,
+            img_seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        save_output_img(out_imgs[0], img_seed)
+        generated_imgs.extend(out_imgs)
+        seeds.append(img_seed)
+        img2img_obj.log += "\n"
+
+    total_time = time.time() - start_time
+    text_output = f"prompt={args.prompts}"
+    text_output += f"\nnegative prompt={args.negative_prompts}"
+    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+    text_output += f"\nscheduler={args.scheduler}, device={device}"
+    text_output += f"\nsteps={steps}, strength={args.strength}, guidance_scale={guidance_scale}, seed={seeds}"
+    text_output += f"\nsize={height}x{width}, batch_count={batch_count}, batch_size={batch_size}, max_length={args.max_length}"
+    text_output += img2img_obj.log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    return generated_imgs, text_output
+
+
+if __name__ == "__main__":
+    if args.clear_all:
+        clear_all()
+
+    if args.img_path is None:
+        print("Flag --img_path is required.")
+        exit()
+
+    # When the models get uploaded, it should be default to False.
+    args.import_mlir = True
+
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    set_init_device_flags()
+    schedulers = get_schedulers(args.hf_model_id)
+    if args.scheduler != "PNDM":
+        if "Shark" in args.scheduler:
+            print(
+                f"SharkEulerDiscrete scheduler not supported. Switching to PNDM scheduler"
+            )
+            args.scheduler = "PNDM"
+        else:
+            sys.exit(
+                "Img2Img works best with PNDM scheduler. Other schedulers are not supported yet."
+            )
+
+    scheduler_obj = schedulers[args.scheduler]
+    image = Image.open(args.img_path).convert("RGB")
+    seed = utils.sanitize_seed(args.seed)
+
+    # Adjust for height and width based on model
+
+    img2img_obj = Image2ImagePipeline.from_pretrained(
+        scheduler_obj,
+        args.import_mlir,
+        args.hf_model_id,
+        args.ckpt_loc,
+        args.custom_vae,
+        args.precision,
+        args.max_length,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.use_base_vae,
+        args.use_tuned,
+        low_cpu_mem_usage=args.low_cpu_mem_usage,
+    )
+
+    start_time = time.time()
+    generated_imgs = img2img_obj.generate_images(
+        args.prompts,
+        args.negative_prompts,
+        image,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.steps,
+        args.strength,
+        args.guidance_scale,
+        seed,
+        args.max_length,
+        dtype,
+        args.use_base_vae,
+        cpu_scheduling,
+    )
+    total_time = time.time() - start_time
+    text_output = f"prompt={args.prompts}"
+    text_output += f"\nnegative prompt={args.negative_prompts}"
+    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+    text_output += f"\nscheduler={args.scheduler}, device={args.device}"
+    text_output += f"\nsteps={args.steps}, strength={args.strength}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
+    text_output += (
+        f", batch size={args.batch_size}, max_length={args.max_length}"
+    )
+    text_output += img2img_obj.log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    save_output_img(generated_imgs[0], seed)
+    print(text_output)

apps/stable_diffusion/scripts/inpaint.py

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

apps/stable_diffusion/scripts/outpaint.py

@@ -0,0 +1,275 @@
+import sys
+import torch
+import time
+from PIL import Image
+from dataclasses import dataclass
+from apps.stable_diffusion.src import (
+    args,
+    OutpaintPipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+
+
+@dataclass
+class Config:
+    model_id: str
+    ckpt_loc: str
+    precision: str
+    batch_size: int
+    max_length: int
+    height: int
+    width: int
+    device: str
+
+
+outpaint_obj = None
+config_obj = None
+schedulers = None
+
+
+# Exposed to UI.
+def outpaint_inf(
+    prompt: str,
+    negative_prompt: str,
+    init_image: str,
+    pixels: int,
+    mask_blur: int,
+    directions: list,
+    noise_q: float,
+    color_variation: float,
+    height: int,
+    width: int,
+    steps: int,
+    guidance_scale: float,
+    seed: int,
+    batch_count: int,
+    batch_size: int,
+    scheduler: str,
+    custom_model: str,
+    hf_model_id: str,
+    precision: str,
+    device: str,
+    max_length: int,
+    save_metadata_to_json: bool,
+    save_metadata_to_png: bool,
+):
+    global outpaint_obj
+    global config_obj
+    global schedulers
+
+    args.prompts = [prompt]
+    args.negative_prompts = [negative_prompt]
+    args.guidance_scale = guidance_scale
+    args.steps = steps
+    args.scheduler = scheduler
+    args.img_path = init_image
+
+    # set ckpt_loc and hf_model_id.
+    types = (
+        ".ckpt",
+        ".safetensors",
+    )  # the tuple of file types
+    args.ckpt_loc = ""
+    args.hf_model_id = ""
+    if custom_model == "None":
+        if not hf_model_id:
+            return (
+                None,
+                "Please provide either custom model or huggingface model ID, both must not be empty",
+            )
+        args.hf_model_id = hf_model_id
+    elif ".ckpt" in custom_model or ".safetensors" in custom_model:
+        args.ckpt_loc = custom_model
+    else:
+        args.hf_model_id = custom_model
+
+    args.save_metadata_to_json = save_metadata_to_json
+    args.write_metadata_to_png = save_metadata_to_png
+
+    dtype = torch.float32 if precision == "fp32" else torch.half
+    cpu_scheduling = not scheduler.startswith("Shark")
+    new_config_obj = Config(
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        height,
+        width,
+        device,
+    )
+    if not outpaint_obj or config_obj != new_config_obj:
+        config_obj = new_config_obj
+        args.precision = precision
+        args.batch_size = batch_size
+        args.max_length = max_length
+        args.height = height
+        args.width = width
+        args.device = device.split("=>", 1)[1].strip()
+        args.iree_vulkan_target_triple = ""
+        args.use_tuned = True
+        args.import_mlir = False
+        set_init_device_flags()
+        model_id = (
+            args.hf_model_id
+            if args.hf_model_id
+            else "stabilityai/stable-diffusion-2-inpainting"
+        )
+        schedulers = get_schedulers(model_id)
+        scheduler_obj = schedulers[scheduler]
+        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,
+        )
+
+    outpaint_obj.scheduler = schedulers[scheduler]
+
+    start_time = time.time()
+    outpaint_obj.log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+    image = Image.open(args.img_path)
+
+    left = True if "left" in directions else False
+    right = True if "right" in directions else False
+    top = True if "up" in directions else False
+    bottom = True if "down" in directions else False
+
+    for i in range(batch_count):
+        if i > 0:
+            img_seed = utils.sanitize_seed(-1)
+        out_imgs = outpaint_obj.generate_images(
+            prompt,
+            negative_prompt,
+            image,
+            args.pixels,
+            args.mask_blur,
+            left,
+            right,
+            top,
+            bottom,
+            noise_q,
+            color_variation,
+            batch_size,
+            height,
+            width,
+            steps,
+            guidance_scale,
+            img_seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        save_output_img(out_imgs[0], img_seed)
+        generated_imgs.extend(out_imgs)
+        seeds.append(img_seed)
+        outpaint_obj.log += "\n"
+
+    total_time = time.time() - start_time
+    text_output = f"prompt={args.prompts}"
+    text_output += f"\nnegative prompt={args.negative_prompts}"
+    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+    text_output += f"\nscheduler={args.scheduler}, device={device}"
+    text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seeds}"
+    text_output += f"\nsize={args.height}x{args.width}, batch-count={batch_count}, batch-size={args.batch_size}, max_length={args.max_length}"
+    text_output += outpaint_obj.log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    return generated_imgs, text_output
+
+
+if __name__ == "__main__":
+    if args.clear_all:
+        clear_all()
+
+    if args.img_path is None:
+        print("Flag --img_path is required.")
+        exit()
+    if "inpaint" not in args.hf_model_id:
+        print("Please use inpainting model with --hf_model_id.")
+        exit()
+
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    set_init_device_flags()
+    schedulers = get_schedulers(args.hf_model_id)
+    scheduler_obj = schedulers[args.scheduler]
+    seed = args.seed
+    image = Image.open(args.img_path)
+
+    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,
+    )
+
+    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_output_img(generated_imgs[0], seed)
+        print(text_output)

apps/stable_diffusion/scripts/telegram_bot.py

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

apps/stable_diffusion/scripts/txt2img.py

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

apps/stable_diffusion/shark_sd.spec

@@ -0,0 +1,78 @@
+# -*- mode: python ; coding: utf-8 -*-
+from PyInstaller.utils.hooks import collect_data_files
+from PyInstaller.utils.hooks import copy_metadata
+
+import sys ; sys.setrecursionlimit(sys.getrecursionlimit() * 5)
+
+datas = []
+datas += collect_data_files('torch')
+datas += copy_metadata('torch')
+datas += copy_metadata('tqdm')
+datas += copy_metadata('regex')
+datas += copy_metadata('requests')
+datas += copy_metadata('packaging')
+datas += copy_metadata('filelock')
+datas += copy_metadata('numpy')
+datas += copy_metadata('tokenizers')
+datas += copy_metadata('importlib_metadata')
+datas += copy_metadata('torch-mlir')
+datas += copy_metadata('omegaconf')
+datas += copy_metadata('safetensors')
+datas += collect_data_files('diffusers')
+datas += collect_data_files('transformers')
+datas += collect_data_files('gradio')
+datas += collect_data_files('iree')
+datas += collect_data_files('google-cloud-storage')
+datas += collect_data_files('shark')
+datas += [
+         ( 'src/utils/resources/prompts.json', 'resources' ),
+         ( 'src/utils/resources/model_db.json', 'resources' ),
+         ( 'src/utils/resources/opt_flags.json', 'resources' ),
+         ( 'src/utils/resources/base_model.json', 'resources' ),
+         ( 'web/ui/css/*', 'ui/css' ),
+         ( 'web/ui/logos/*', 'logos' )
+         ]
+
+binaries = []
+
+block_cipher = None
+
+
+a = Analysis(
+    ['web/index.py'],
+    pathex=['.'],
+    binaries=binaries,
+    datas=datas,
+    hiddenimports=['shark', 'shark.shark_inference', 'apps'],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+    noarchive=False,
+)
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    [],
+    name='shark_sd',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)

apps/stable_diffusion/shark_sd_cli.spec

@@ -15,18 +15,20 @@ datas += copy_metadata('filelock')
 datas += copy_metadata('numpy')
 datas += copy_metadata('tokenizers')
 datas += copy_metadata('importlib_metadata')
-datas += copy_metadata('torchvision')
 datas += copy_metadata('torch-mlir')
-datas += copy_metadata('diffusers')
-datas += copy_metadata('transformers')
+datas += copy_metadata('omegaconf')
+datas += copy_metadata('safetensors')
+datas += collect_data_files('diffusers')
+datas += collect_data_files('transformers')
 datas += collect_data_files('gradio')
 datas += collect_data_files('iree')
 datas += collect_data_files('google-cloud-storage')
 datas += collect_data_files('shark')
 datas += [
-         ( 'models/stable_diffusion/resources/prompts.json', 'resources' ),
-         ( 'models/stable_diffusion/resources/model_db.json', 'resources' ),
-         ( 'models/stable_diffusion/logos/*', 'logos' )
+         ( '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 = []
@@ -35,11 +37,11 @@ block_cipher = None
 
 
 a = Analysis(
-    ['index.py'],
+    ['scripts/txt2img.py'],
     pathex=['.'],
     binaries=binaries,
     datas=datas,
-    hiddenimports=['shark', 'shark.*', 'shark.shark_inference', 'shark_inference', 'iree.tools.core', 'gradio'],
+    hiddenimports=['shark', 'shark.shark_inference', 'apps'],
     hookspath=[],
     hooksconfig={},
     runtime_hooks=[],
@@ -58,7 +60,7 @@ exe = EXE(
     a.zipfiles,
     a.datas,
     [],
-    name='shark_sd',
+    name='shark_sd_cli',
     debug=False,
     bootloader_ignore_signals=False,
     strip=False,

apps/stable_diffusion/src/__init__.py

@@ -0,0 +1,15 @@
+from apps.stable_diffusion.src.utils import (
+    args,
+    set_init_device_flags,
+    prompt_examples,
+    get_available_devices,
+    clear_all,
+    save_output_img,
+)
+from apps.stable_diffusion.src.pipelines import (
+    Text2ImagePipeline,
+    Image2ImagePipeline,
+    InpaintPipeline,
+    OutpaintPipeline,
+)
+from apps.stable_diffusion.src.schedulers import get_schedulers

apps/stable_diffusion/src/models/__init__.py

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

apps/stable_diffusion/src/models/model_wrappers.py

@@ -0,0 +1,395 @@
+from diffusers import AutoencoderKL, UNet2DConditionModel
+from transformers import CLIPTextModel
+from collections import defaultdict
+import torch
+import safetensors.torch
+import traceback
+import sys
+from apps.stable_diffusion.src.utils import (
+    compile_through_fx,
+    get_opt_flags,
+    base_models,
+    args,
+    fetch_or_delete_vmfbs,
+    preprocessCKPT,
+    get_path_to_diffusers_checkpoint,
+    fetch_and_update_base_model_id,
+    get_path_stem,
+    get_extended_name,
+)
+
+
+# These shapes are parameter dependent.
+def replace_shape_str(shape, max_len, width, height, batch_size):
+    new_shape = []
+    for i in range(len(shape)):
+        if shape[i] == "max_len":
+            new_shape.append(max_len)
+        elif shape[i] == "height":
+            new_shape.append(height)
+        elif shape[i] == "width":
+            new_shape.append(width)
+        elif isinstance(shape[i], str):
+            mul_val = int(shape[i].split("*")[0])
+            if "batch_size" in shape[i]:
+                new_shape.append(batch_size * mul_val)
+            elif "height" in shape[i]:
+                new_shape.append(height * mul_val)
+            elif "width" in shape[i]:
+                new_shape.append(width * mul_val)
+        else:
+            new_shape.append(shape[i])
+    return new_shape
+
+
+# Get the input info for various models i.e. "unet", "clip", "vae", "vae_encode".
+def get_input_info(model_info, max_len, width, height, batch_size):
+    dtype_config = {"f32": torch.float32, "i64": torch.int64}
+    input_map = defaultdict(list)
+    for k in model_info:
+        for inp in model_info[k]:
+            shape = model_info[k][inp]["shape"]
+            dtype = dtype_config[model_info[k][inp]["dtype"]]
+            tensor = None
+            if isinstance(shape, list):
+                clean_shape = replace_shape_str(
+                    shape, max_len, width, height, batch_size
+                )
+                if dtype == torch.int64:
+                    tensor = torch.randint(1, 3, tuple(clean_shape))
+                else:
+                    tensor = torch.randn(*clean_shape).to(dtype)
+            elif isinstance(shape, int):
+                tensor = torch.tensor(shape).to(dtype)
+            else:
+                sys.exit("shape isn't specified correctly.")
+            input_map[k].append(tensor)
+    return input_map
+
+
+class SharkifyStableDiffusionModel:
+    def __init__(
+        self,
+        model_id: str,
+        custom_weights: str,
+        custom_vae: str,
+        precision: str,
+        max_len: int = 64,
+        width: int = 512,
+        height: int = 512,
+        batch_size: int = 1,
+        use_base_vae: bool = False,
+        use_tuned: bool = False,
+        low_cpu_mem_usage: bool = False
+    ):
+        self.check_params(max_len, width, height)
+        self.max_len = max_len
+        self.height = height // 8
+        self.width = width // 8
+        self.batch_size = batch_size
+        self.custom_weights = custom_weights
+        if custom_weights != "":
+            assert custom_weights.lower().endswith(
+                (".ckpt", ".safetensors")
+            ), "checkpoint files supported can be any of [.ckpt, .safetensors] type"
+            custom_weights = get_path_to_diffusers_checkpoint(custom_weights)
+        self.model_id = model_id if custom_weights == "" else custom_weights
+        # TODO: remove the following line when stable-diffusion-2-1 works
+        if self.model_id == "stabilityai/stable-diffusion-2-1":
+            self.model_id = "stabilityai/stable-diffusion-2-1-base"
+        self.custom_vae = custom_vae
+        self.precision = precision
+        self.base_vae = use_base_vae
+        self.model_name = (
+            str(batch_size)
+            + "_"
+            + str(max_len)
+            + "_"
+            + str(height)
+            + "_"
+            + str(width)
+            + "_"
+            + precision
+        )
+        self.use_tuned = use_tuned
+        if use_tuned:
+            self.model_name = self.model_name + "_tuned"
+        self.model_name = self.model_name + "_" + get_path_stem(self.model_id)
+        self.low_cpu_mem_usage = low_cpu_mem_usage
+
+    def get_extended_name_for_all_model(self):
+        model_name = {}
+        sub_model_list = ["clip", "unet", "vae", "vae_encode"]
+        for model in sub_model_list:
+            sub_model = model
+            model_config = self.model_name
+            if "vae" == model:
+                if self.custom_vae != "":
+                    model_config = model_config + get_path_stem(self.custom_vae)
+                if self.base_vae:
+                    sub_model = "base_vae"
+            model_name[model] = get_extended_name(sub_model + model_config)
+        return model_name
+
+    def check_params(self, max_len, width, height):
+        if not (max_len >= 32 and max_len <= 77):
+            sys.exit("please specify max_len in the range [32, 77].")
+        if not (width % 8 == 0 and width >= 384):
+            sys.exit("width should be greater than 384 and multiple of 8")
+        if not (height % 8 == 0 and height >= 384):
+            sys.exit("height should be greater than 384 and multiple of 8")
+
+    def get_vae_encode(self):
+        class VaeEncodeModel(torch.nn.Module):
+            def __init__(self, model_id=self.model_id, 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 self.precision == "fp16" else False
+        shark_vae_encode = compile_through_fx(
+            vae_encode,
+            inputs,
+            is_f16=is_f16,
+            use_tuned=self.use_tuned,
+            model_name=self.model_name["vae_encode"],
+            extra_args=get_opt_flags("vae", precision=self.precision),
+        )
+        return shark_vae_encode
+
+    def get_vae(self):
+        class VaeModel(torch.nn.Module):
+            def __init__(self, model_id=self.model_id, base_vae=self.base_vae, custom_vae=self.custom_vae, 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 self.precision == "fp16" else False
+        shark_vae = compile_through_fx(
+            vae,
+            inputs,
+            is_f16=is_f16,
+            use_tuned=self.use_tuned,
+            model_name=self.model_name["vae"],
+            extra_args=get_opt_flags("vae", precision=self.precision),
+        )
+        return shark_vae
+
+    def get_unet(self):
+        class UnetModel(torch.nn.Module):
+            def __init__(self, model_id=self.model_id, 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, 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"])
+        input_mask = [True, True, True, False]
+        shark_unet = compile_through_fx(
+            unet,
+            inputs,
+            model_name=self.model_name["unet"],
+            is_f16=is_f16,
+            f16_input_mask=input_mask,
+            use_tuned=self.use_tuned,
+            extra_args=get_opt_flags("unet", precision=self.precision),
+        )
+        return shark_unet
+
+    def get_clip(self):
+        class CLIPText(torch.nn.Module):
+            def __init__(self, model_id=self.model_id, low_cpu_mem_usage=False):
+                super().__init__()
+                self.text_encoder = CLIPTextModel.from_pretrained(
+                    model_id,
+                    subfolder="text_encoder",
+                    low_cpu_mem_usage=low_cpu_mem_usage,
+                )
+
+            def forward(self, input):
+                return self.text_encoder(input)[0]
+
+        clip_model = CLIPText(low_cpu_mem_usage=self.low_cpu_mem_usage)
+        shark_clip = compile_through_fx(
+            clip_model,
+            tuple(self.inputs["clip"]),
+            model_name=self.model_name["clip"],
+            extra_args=get_opt_flags("clip", precision="fp32"),
+        )
+        return shark_clip
+
+    def process_custom_vae(self):
+        custom_vae = self.custom_vae.lower()
+        if not custom_vae.endswith((".ckpt", ".safetensors")):
+            return self.custom_vae
+        try:
+            preprocessCKPT(self.custom_vae)
+            return get_path_to_diffusers_checkpoint(self.custom_vae)
+        except:
+            print("Processing standalone Vae checkpoint")
+            vae_checkpoint = None
+            vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+            if custom_vae.endswith(".ckpt"):
+                vae_checkpoint = torch.load(self.custom_vae, map_location="cpu")
+            else:
+                vae_checkpoint = safetensors.torch.load_file(self.custom_vae, device="cpu")
+            if "state_dict" in vae_checkpoint:
+                vae_checkpoint = vae_checkpoint["state_dict"]
+            vae_dict = {k: v for k, v in vae_checkpoint.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+            return vae_dict
+        
+            
+    # Compiles Clip, Unet and Vae with `base_model_id` as defining their input
+    # configiration.
+    def compile_all(self, base_model_id, need_vae_encode):
+        self.inputs = get_input_info(
+            base_models[base_model_id],
+            self.max_len,
+            self.width,
+            self.height,
+            self.batch_size,
+        )
+        compiled_unet = self.get_unet()
+        if self.custom_vae != "":
+            print("Plugging in custom Vae")
+        compiled_vae = self.get_vae()
+        compiled_clip = self.get_clip()
+        if need_vae_encode:
+            compiled_vae_encode = self.get_vae_encode()
+            return compiled_clip, compiled_unet, compiled_vae, compiled_vae_encode
+
+        return compiled_clip, compiled_unet, compiled_vae
+
+    def __call__(self):
+        # Step 1:
+        # --  Fetch all vmfbs for the model, if present, else delete the lot.
+        need_vae_encode = args.img_path is not None
+        self.model_name = self.get_extended_name_for_all_model()
+        vmfbs = fetch_or_delete_vmfbs(self.model_name, need_vae_encode, self.precision)   
+        if vmfbs[0]:
+            # -- If all vmfbs are indeed present, we also try and fetch the base
+            #    model configuration for running SD with custom checkpoints.
+            if self.custom_weights != "":
+                args.hf_model_id = fetch_and_update_base_model_id(self.custom_weights)
+            if args.hf_model_id == "":
+                sys.exit("Base model configuration for the custom model is missing. Use `--clear_all` and re-run.")
+            print("Loaded vmfbs from cache and successfully fetched base model configuration.")
+            if not need_vae_encode:
+                return vmfbs[:3]
+            return vmfbs
+
+        # Step 2:
+        # -- If vmfbs weren't found, we try to see if the base model configuration
+        #    for the required SD run is known to us and bypass the retry mechanism.
+        model_to_run = ""
+        if self.custom_weights != "":
+            model_to_run = self.custom_weights
+            assert self.custom_weights.lower().endswith(
+                (".ckpt", ".safetensors")
+            ), "checkpoint files supported can be any of [.ckpt, .safetensors] type"
+            preprocessCKPT(self.custom_weights)
+        else:
+            model_to_run = args.hf_model_id
+        # For custom Vae user can provide either the repo-id or a checkpoint file,
+        # and for a checkpoint file we'd need to process it via Diffusers' script.
+        self.custom_vae = self.process_custom_vae()
+        base_model_fetched = fetch_and_update_base_model_id(model_to_run)
+        if base_model_fetched != "":
+            print("Compiling all the models with the fetched base model configuration.")
+            if args.ckpt_loc != "":
+                args.hf_model_id = base_model_fetched
+            return self.compile_all(base_model_fetched, need_vae_encode)
+
+        # Step 3:
+        # -- This is the retry mechanism where the base model's configuration is not
+        #    known to us and figure that out by trial and error.
+        print("Inferring base model configuration.")
+        for model_id in base_models:
+            try:
+                if need_vae_encode:
+                    compiled_clip, compiled_unet, compiled_vae, compiled_vae_encode = self.compile_all(model_id, need_vae_encode)
+                else:
+                    compiled_clip, compiled_unet, compiled_vae = self.compile_all(model_id, need_vae_encode)
+            except Exception as e:
+                print("Retrying with a different base model configuration")
+                continue
+            # -- Once a successful compilation has taken place we'd want to store
+            #    the base model's configuration inferred.
+            fetch_and_update_base_model_id(model_to_run, model_id)
+            # This is done just because in main.py we are basing the choice of tokenizer and scheduler
+            # on `args.hf_model_id`. Since now, we don't maintain 1:1 mapping of variants and the base
+            # model and rely on retrying method to find the input configuration, we should also update
+            # the knowledge of base model id accordingly into `args.hf_model_id`.
+            if args.ckpt_loc != "":
+                args.hf_model_id = model_id
+            if need_vae_encode:
+                return (
+                    compiled_clip,
+                    compiled_unet,
+                    compiled_vae,
+                    compiled_vae_encode,
+                )
+            return compiled_clip, compiled_unet, compiled_vae
+        sys.exit(
+            "Cannot compile the model. Please create an issue with the detailed log at https://github.com/nod-ai/SHARK/issues"
+        )

apps/stable_diffusion/src/models/opt_params.py

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

apps/stable_diffusion/src/pipelines/__init__.py

@@ -0,0 +1,12 @@
+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,
+)

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_img2img.py

@@ -0,0 +1,171 @@
+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,
+)
+
+
+class Image2ImagePipeline(StableDiffusionPipeline):
+    def __init__(
+        self,
+        vae_encode: SharkInference,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+            DEISMultistepScheduler,
+        ],
+    ):
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler)
+        self.vae_encode = vae_encode
+
+    def prepare_image_latents(
+        self,
+        image,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        strength,
+        dtype,
+    ):
+        # Pre process image -> get image encoded -> process latents
+
+        # TODO: process with variable HxW combos
+
+        # Pre process image
+        image = image.resize((width, height))
+        image_arr = np.stack([np.array(i) for i in (image,)], axis=0)
+        image_arr = image_arr / 255.0
+        image_arr = torch.from_numpy(image_arr).permute(0, 3, 1, 2).to(dtype)
+        image_arr = 2 * (image_arr - 0.5)
+
+        # 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):
+        vae_encode_start = time.time()
+        latents = self.vae_encode("forward", input_image)
+        vae_inf_time = (time.time() - vae_encode_start) * 1000
+        self.log += f"\nVAE Encode Inference time (ms): {vae_inf_time:.3f}"
+
+        return latents
+
+    def generate_images(
+        self,
+        prompts,
+        neg_prompts,
+        image,
+        batch_size,
+        height,
+        width,
+        num_inference_steps,
+        strength,
+        guidance_scale,
+        seed,
+        max_length,
+        dtype,
+        use_base_vae,
+        cpu_scheduling,
+    ):
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get text embeddings from prompts
+        text_embeddings = self.encode_prompts(prompts, neg_prompts, max_length)
+
+        # guidance scale as a float32 tensor.
+        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
+
+        # Prepare input image latent
+        image_latents, 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 = []
+        for i in tqdm(range(0, latents.shape[0], batch_size)):
+            imgs = self.decode_latents(
+                latents=latents[i : i + batch_size],
+                use_base_vae=use_base_vae,
+                cpu_scheduling=cpu_scheduling,
+            )
+            all_imgs.extend(imgs)
+
+        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_inpaint.py

@@ -0,0 +1,233 @@
+import torch
+from tqdm.auto import tqdm
+import numpy as np
+from random import randint
+from PIL import Image
+from transformers import CLIPTokenizer
+from typing import Union
+from shark.shark_inference import SharkInference
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+    DEISMultistepScheduler,
+)
+from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+    StableDiffusionPipeline,
+)
+
+
+class InpaintPipeline(StableDiffusionPipeline):
+    def __init__(
+        self,
+        vae_encode: SharkInference,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+            DEISMultistepScheduler,
+        ],
+    ):
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler)
+        self.vae_encode = vae_encode
+
+    def prepare_mask_and_masked_image(self, image, mask, height, width):
+        # preprocess image
+        image = image.resize((width, height))
+        mask = mask.resize((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
+
+    def prepare_latents(
+        self,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        dtype,
+    ):
+        latents = torch.randn(
+            (
+                batch_size,
+                4,
+                height // 8,
+                width // 8,
+            ),
+            generator=generator,
+            dtype=torch.float32,
+        ).to(dtype)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+        self.scheduler.is_scale_input_called = True
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_mask_latents(
+        self,
+        mask,
+        masked_image,
+        batch_size,
+        height,
+        width,
+        dtype,
+    ):
+        mask = torch.nn.functional.interpolate(
+            mask, size=(height // 8, width // 8)
+        )
+        mask = mask.to(dtype)
+
+        masked_image = masked_image.to(dtype)
+        masked_image_latents = self.vae_encode("forward", (masked_image,))
+        masked_image_latents = torch.from_numpy(masked_image_latents)
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(
+                batch_size // masked_image_latents.shape[0], 1, 1, 1
+            )
+        return mask, masked_image_latents
+
+    def generate_images(
+        self,
+        prompts,
+        neg_prompts,
+        image,
+        mask_image,
+        batch_size,
+        height,
+        width,
+        num_inference_steps,
+        guidance_scale,
+        seed,
+        max_length,
+        dtype,
+        use_base_vae,
+        cpu_scheduling,
+    ):
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get initial latents
+        init_latents = self.prepare_latents(
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+            dtype=dtype,
+        )
+
+        # Get text embeddings from prompts
+        text_embeddings = self.encode_prompts(prompts, neg_prompts, max_length)
+
+        # guidance scale as a float32 tensor.
+        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
+
+        # Preprocess mask and image
+        mask, masked_image = self.prepare_mask_and_masked_image(
+            image, mask_image, height, width
+        )
+
+        # Prepare mask latent variables
+        mask, masked_image_latents = self.prepare_mask_latents(
+            mask=mask,
+            masked_image=masked_image,
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            dtype=dtype,
+        )
+
+        # Get Image latents
+        latents = self.produce_img_latents(
+            latents=init_latents,
+            text_embeddings=text_embeddings,
+            guidance_scale=guidance_scale,
+            total_timesteps=self.scheduler.timesteps,
+            dtype=dtype,
+            cpu_scheduling=cpu_scheduling,
+            mask=mask,
+            masked_image_latents=masked_image_latents,
+        )
+
+        # Img latents -> PIL images
+        all_imgs = []
+        for i in tqdm(range(0, latents.shape[0], batch_size)):
+            imgs = self.decode_latents(
+                latents=latents[i : i + batch_size],
+                use_base_vae=use_base_vae,
+                cpu_scheduling=cpu_scheduling,
+            )
+            all_imgs.extend(imgs)
+
+        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_outpaint.py

@@ -0,0 +1,540 @@
+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
+
+
+class OutpaintPipeline(StableDiffusionPipeline):
+    def __init__(
+        self,
+        vae_encode: SharkInference,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+            DEISMultistepScheduler,
+        ],
+    ):
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler)
+        self.vae_encode = vae_encode
+
+    def prepare_latents(
+        self,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        dtype,
+    ):
+        latents = torch.randn(
+            (
+                batch_size,
+                4,
+                height // 8,
+                width // 8,
+            ),
+            generator=generator,
+            dtype=torch.float32,
+        ).to(dtype)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+        self.scheduler.is_scale_input_called = True
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_mask_and_masked_image(self, image, mask, mask_blur):
+        if mask_blur > 0:
+            mask = mask.filter(ImageFilter.GaussianBlur(mask_blur))
+        image = image.resize((512, 512))
+        mask = mask.resize((512, 512))
+
+        # 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)
+
+        masked_image = masked_image.to(dtype)
+        masked_image_latents = self.vae_encode("forward", (masked_image,))
+        masked_image_latents = torch.from_numpy(masked_image_latents)
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(
+                batch_size // masked_image_latents.shape[0], 1, 1, 1
+            )
+        return mask, masked_image_latents
+
+    def 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 from prompts
+        text_embeddings = self.encode_prompts(prompts, neg_prompts, max_length)
+
+        # guidance scale as a float32 tensor.
+        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
+
+        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
+            )
+
+            # Prepare mask latent variables
+            mask, masked_image_latents = self.prepare_mask_latents(
+                mask=mask,
+                masked_image=masked_image,
+                batch_size=batch_size,
+                height=height,
+                width=width,
+                dtype=dtype,
+            )
+
+            # Get Image latents
+            latents = self.produce_img_latents(
+                latents=init_latents,
+                text_embeddings=text_embeddings,
+                guidance_scale=guidance_scale,
+                total_timesteps=self.scheduler.timesteps,
+                dtype=dtype,
+                cpu_scheduling=cpu_scheduling,
+                mask=mask,
+                masked_image_latents=masked_image_latents,
+            )
+
+            # Img latents -> PIL images
+            all_imgs = []
+            for i in tqdm(range(0, latents.shape[0], batch_size)):
+                imgs = self.decode_latents(
+                    latents=latents[i : i + batch_size],
+                    use_base_vae=use_base_vae,
+                    cpu_scheduling=cpu_scheduling,
+                )
+                all_imgs.extend(imgs)
+
+            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_txt2img.py

@@ -0,0 +1,139 @@
+import torch
+from tqdm.auto import tqdm
+import numpy as np
+from random import randint
+from transformers import CLIPTokenizer
+from typing import Union
+from shark.shark_inference import SharkInference
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    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,
+)
+
+
+class Text2ImagePipeline(StableDiffusionPipeline):
+    def __init__(
+        self,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            KDPM2DiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+            DEISMultistepScheduler,
+        ],
+    ):
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler)
+
+    def prepare_latents(
+        self,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        dtype,
+    ):
+        latents = torch.randn(
+            (
+                batch_size,
+                4,
+                height // 8,
+                width // 8,
+            ),
+            generator=generator,
+            dtype=torch.float32,
+        ).to(dtype)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+        self.scheduler.is_scale_input_called = True
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def generate_images(
+        self,
+        prompts,
+        neg_prompts,
+        batch_size,
+        height,
+        width,
+        num_inference_steps,
+        guidance_scale,
+        seed,
+        max_length,
+        dtype,
+        use_base_vae,
+        cpu_scheduling,
+    ):
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        # TODO: Wouldn't it be preferable to just report an error instead of modifying the seed on the fly?
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get initial latents
+        init_latents = self.prepare_latents(
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+            dtype=dtype,
+        )
+
+        # Get text embeddings from prompts
+        text_embeddings = self.encode_prompts(prompts, neg_prompts, max_length)
+
+        # guidance scale as a float32 tensor.
+        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
+
+        # Get Image latents
+        latents = self.produce_img_latents(
+            latents=init_latents,
+            text_embeddings=text_embeddings,
+            guidance_scale=guidance_scale,
+            total_timesteps=self.scheduler.timesteps,
+            dtype=dtype,
+            cpu_scheduling=cpu_scheduling,
+        )
+
+        # Img latents -> PIL images
+        all_imgs = []
+        for i in tqdm(range(0, latents.shape[0], batch_size)):
+            imgs = self.decode_latents(
+                latents=latents[i : i + batch_size],
+                use_base_vae=use_base_vae,
+                cpu_scheduling=cpu_scheduling,
+            )
+            all_imgs.extend(imgs)
+
+        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_utils.py

@@ -0,0 +1,276 @@
+import torch
+import numpy as np
+from transformers import CLIPTokenizer
+from PIL import Image
+from tqdm.auto import tqdm
+import time
+from typing import Union
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    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_encode,
+    get_vae,
+    get_clip,
+    get_unet,
+    get_tokenizer,
+)
+from apps.stable_diffusion.src.utils import (
+    start_profiling,
+    end_profiling,
+)
+
+
+class StableDiffusionPipeline:
+    def __init__(
+        self,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            KDPM2DiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+            DEISMultistepScheduler,
+        ],
+    ):
+        self.vae = vae
+        self.text_encoder = text_encoder
+        self.tokenizer = tokenizer
+        self.unet = unet
+        self.scheduler = scheduler
+        # TODO: Implement using logging python utility.
+        self.log = ""
+
+    def encode_prompts(self, prompts, neg_prompts, max_length):
+        # Tokenize text and get embeddings
+        text_input = self.tokenizer(
+            prompts,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        # Get unconditional embeddings as well
+        uncond_input = self.tokenizer(
+            neg_prompts,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input = torch.cat([uncond_input.input_ids, text_input.input_ids])
+
+        clip_inf_start = time.time()
+        text_embeddings = self.text_encoder("forward", (text_input,))
+        clip_inf_time = (time.time() - clip_inf_start) * 1000
+        self.log += f"\nClip Inference time (ms) = {clip_inf_time:.3f}"
+
+        return text_embeddings
+
+    def decode_latents(self, latents, use_base_vae, cpu_scheduling):
+        if use_base_vae:
+            latents = 1 / 0.18215 * latents
+
+        latents_numpy = latents
+        if cpu_scheduling:
+            latents_numpy = latents.detach().numpy()
+
+        profile_device = start_profiling(file_path="vae.rdc")
+        vae_start = time.time()
+        images = self.vae("forward", (latents_numpy,))
+        vae_inf_time = (time.time() - vae_start) * 1000
+        end_profiling(profile_device)
+        self.log += f"\nVAE Inference time (ms): {vae_inf_time:.3f}"
+
+        if use_base_vae:
+            images = torch.from_numpy(images)
+            images = (images.detach().cpu() * 255.0).numpy()
+            images = images.round()
+
+        images = torch.from_numpy(images).to(torch.uint8).permute(0, 2, 3, 1)
+        pil_images = [Image.fromarray(image) for image in images.numpy()]
+        return pil_images
+
+    def produce_img_latents(
+        self,
+        latents,
+        text_embeddings,
+        guidance_scale,
+        total_timesteps,
+        dtype,
+        cpu_scheduling,
+        mask=None,
+        masked_image_latents=None,
+        return_all_latents=False,
+    ):
+        step_time_sum = 0
+        latent_history = [latents]
+        text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
+        text_embeddings_numpy = text_embeddings.detach().numpy()
+        for i, t in tqdm(enumerate(total_timesteps)):
+            step_start_time = time.time()
+            timestep = torch.tensor([t]).to(dtype).detach().numpy()
+            latent_model_input = self.scheduler.scale_model_input(latents, t)
+            if mask is not None and masked_image_latents is not None:
+                latent_model_input = torch.cat(
+                    [
+                        torch.from_numpy(np.asarray(latent_model_input)),
+                        mask,
+                        masked_image_latents,
+                    ],
+                    dim=1,
+                ).to(dtype)
+            if cpu_scheduling:
+                latent_model_input = latent_model_input.detach().numpy()
+
+            # Profiling Unet.
+            profile_device = start_profiling(file_path="unet.rdc")
+            noise_pred = self.unet(
+                "forward",
+                (
+                    latent_model_input,
+                    timestep,
+                    text_embeddings_numpy,
+                    guidance_scale,
+                ),
+                send_to_host=False,
+            )
+            end_profiling(profile_device)
+
+            if cpu_scheduling:
+                noise_pred = torch.from_numpy(noise_pred.to_host())
+                latents = self.scheduler.step(
+                    noise_pred, t, latents
+                ).prev_sample
+            else:
+                latents = self.scheduler.step(noise_pred, t, latents)
+
+            latent_history.append(latents)
+            step_time = (time.time() - step_start_time) * 1000
+            #  self.log += (
+            #      f"\nstep = {i} | timestep = {t} | time = {step_time:.2f}ms"
+            #  )
+            step_time_sum += step_time
+
+        avg_step_time = step_time_sum / len(total_timesteps)
+        self.log += f"\nAverage step time: {avg_step_time}ms/it"
+
+        if not return_all_latents:
+            return latents
+        all_latents = torch.cat(latent_history, dim=0)
+        return all_latents
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            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,
+        low_cpu_mem_usage: bool = False,
+    ):
+        if import_mlir:
+            mlir_import = SharkifyStableDiffusionModel(
+                model_id,
+                ckpt_loc,
+                custom_vae,
+                precision,
+                max_len=max_length,
+                batch_size=batch_size,
+                height=height,
+                width=width,
+                use_base_vae=use_base_vae,
+                use_tuned=use_tuned,
+                low_cpu_mem_usage=low_cpu_mem_usage,
+            )
+            if cls.__name__ in [
+                "Image2ImagePipeline",
+                "InpaintPipeline",
+                "OutpaintPipeline",
+            ]:
+                clip, unet, vae, vae_encode = mlir_import()
+                return cls(
+                    vae_encode, vae, clip, get_tokenizer(), unet, scheduler
+                )
+            clip, unet, vae = mlir_import()
+            return cls(vae, clip, get_tokenizer(), unet, scheduler)
+        try:
+            if cls.__name__ in [
+                "Image2ImagePipeline",
+                "InpaintPipeline",
+                "OutpaintPipeline",
+            ]:
+                return cls(
+                    get_vae_encode(),
+                    get_vae(),
+                    get_clip(),
+                    get_tokenizer(),
+                    get_unet(),
+                    scheduler,
+                )
+            return cls(
+                get_vae(), get_clip(), get_tokenizer(), get_unet(), scheduler
+            )
+        except:
+            print("download pipeline failed, falling back to import_mlir")
+            mlir_import = SharkifyStableDiffusionModel(
+                model_id,
+                ckpt_loc,
+                custom_vae,
+                precision,
+                max_len=max_length,
+                batch_size=batch_size,
+                height=height,
+                width=width,
+                use_base_vae=use_base_vae,
+                use_tuned=use_tuned,
+                low_cpu_mem_usage=low_cpu_mem_usage,
+            )
+            if cls.__name__ in [
+                "Image2ImagePipeline",
+                "InpaintPipeline",
+                "OutpaintPipeline",
+            ]:
+                clip, unet, vae, vae_encode = mlir_import()
+                return cls(
+                    vae_encode, vae, clip, get_tokenizer(), unet, scheduler
+                )
+            clip, unet, vae = mlir_import()
+            return cls(vae, clip, get_tokenizer(), unet, scheduler)

apps/stable_diffusion/src/schedulers/__init__.py

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

apps/stable_diffusion/src/schedulers/sd_schedulers.py

@@ -0,0 +1,61 @@
+from diffusers import (
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    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["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

@@ -9,21 +9,13 @@ from diffusers import (
     EulerDiscreteScheduler,
 )
 from diffusers.configuration_utils import register_to_config
-from models.stable_diffusion.utils import compile_through_fx, get_shark_model
-from models.stable_diffusion.stable_args import args
+from apps.stable_diffusion.src.utils import (
+    compile_through_fx,
+    get_shark_model,
+    args,
+)
 import torch
 
-SCHEDULER_BUCKET = "gs://shark_tank/stable_diffusion/schedulers"
-
-model_input = {
-    "euler": {
-        "latent": torch.randn(1, 4, 64, 64),
-        "output": torch.randn(1, 4, 64, 64),
-        "sigma": torch.tensor(1).to(torch.float32),
-        "dt": torch.tensor(1).to(torch.float32),
-    },
-}
-
 
 class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
     @register_to_config
@@ -46,6 +38,22 @@ class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
         )
 
     def compile(self):
+        SCHEDULER_BUCKET = "gs://shark_tank/stable_diffusion/schedulers"
+        BATCH_SIZE = args.batch_size
+
+        model_input = {
+            "euler": {
+                "latent": torch.randn(
+                    BATCH_SIZE, 4, args.height // 8, args.width // 8
+                ),
+                "output": torch.randn(
+                    BATCH_SIZE, 4, args.height // 8, args.width // 8
+                ),
+                "sigma": torch.tensor(1).to(torch.float32),
+                "dt": torch.tensor(1).to(torch.float32),
+            },
+        }
+
         example_latent = model_input["euler"]["latent"]
         example_output = model_input["euler"]["output"]
         if args.precision == "fp16":
@@ -79,12 +87,13 @@ class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
         if sys.platform == "darwin":
             iree_flags.append("-iree-stream-fuse-binding=false")
 
-        if args.import_mlir:
+        def _import(self):
             scaling_model = ScalingModel()
             self.scaling_model = compile_through_fx(
                 scaling_model,
                 (example_latent, example_sigma),
-                model_name="euler_scale_model_input_" + args.precision,
+                model_name=f"euler_scale_model_input_{BATCH_SIZE}_{args.height}_{args.width}"
+                + args.precision,
                 extra_args=iree_flags,
             )
 
@@ -92,23 +101,38 @@ class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
             self.step_model = compile_through_fx(
                 step_model,
                 (example_output, example_sigma, example_latent, example_dt),
-                model_name="euler_step_" + args.precision,
+                model_name=f"euler_step_{BATCH_SIZE}_{args.height}_{args.width}"
+                + args.precision,
                 extra_args=iree_flags,
             )
+
+        if args.import_mlir:
+            _import(self)
+
         else:
-            self.scaling_model = get_shark_model(
-                SCHEDULER_BUCKET,
-                "euler_scale_model_input_" + args.precision,
-                iree_flags,
-            )
-            self.step_model = get_shark_model(
-                SCHEDULER_BUCKET, "euler_step_" + args.precision, iree_flags
-            )
+            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(
+        return self.scaling_model(
+            "forward",
             (
                 sample,
                 sigma,
@@ -120,7 +144,8 @@ class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
         step_index = (self.timesteps == timestep).nonzero().item()
         sigma = self.sigmas[step_index]
         dt = self.sigmas[step_index + 1] - sigma
-        return self.step_model.forward(
+        return self.step_model(
+            "forward",
             (
                 noise_pred,
                 sigma,

apps/stable_diffusion/src/utils/__init__.py

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

apps/stable_diffusion/src/utils/profiler.py

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

apps/stable_diffusion/src/utils/resources.py

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

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

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

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

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

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

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

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

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

apps/stable_diffusion/src/utils/sd_annotation.py

@@ -0,0 +1,238 @@
+import os
+import io
+from shark.model_annotation import model_annotation, create_context
+from shark.iree_utils._common import iree_target_map, run_cmd
+from shark.shark_downloader import (
+    download_model,
+    download_public_file,
+    WORKDIR,
+)
+from shark.parser import shark_args
+from apps.stable_diffusion.src.utils.stable_args import args
+
+
+def get_device():
+    device = (
+        args.device
+        if "://" not in args.device
+        else args.device.split("://")[0]
+    )
+    return device
+
+
+def get_device_args():
+    device = get_device()
+    device_spec_args = []
+    if device == "cuda":
+        from shark.iree_utils.gpu_utils import get_iree_gpu_args
+
+        gpu_flags = get_iree_gpu_args()
+        for flag in gpu_flags:
+            device_spec_args.append(flag)
+    elif device == "vulkan":
+        device_spec_args.append(
+            f"--iree-vulkan-target-triple={args.iree_vulkan_target_triple} "
+        )
+    return device, device_spec_args
+
+
+# Download the model (Unet or VAE fp16) from shark_tank
+def load_model_from_tank():
+    from apps.stable_diffusion.src.models import (
+        get_params,
+        get_variant_version,
+    )
+
+    variant, version = get_variant_version(args.hf_model_id)
+
+    shark_args.local_tank_cache = args.local_tank_cache
+    bucket_key = f"{variant}/untuned"
+    if args.annotation_model == "unet":
+        model_key = f"{variant}/{version}/unet/{args.precision}/length_{args.max_length}/untuned"
+    elif args.annotation_model == "vae":
+        is_base = "/base" if args.use_base_vae else ""
+        model_key = f"{variant}/{version}/vae/{args.precision}/length_77/untuned{is_base}"
+
+    bucket, model_name, iree_flags = get_params(
+        bucket_key, model_key, args.annotation_model, "untuned", args.precision
+    )
+    mlir_model, func_name, inputs, golden_out = download_model(
+        model_name,
+        tank_url=bucket,
+        frontend="torch",
+    )
+    return mlir_model, model_name
+
+
+# Download the tuned config files from shark_tank
+def load_winograd_configs():
+    device = get_device()
+    config_bucket = "gs://shark_tank/sd_tuned/configs/"
+    config_name = f"{args.annotation_model}_winograd_{device}.json"
+    full_gs_url = config_bucket + config_name
+    winograd_config_dir = os.path.join(WORKDIR, "configs", config_name)
+    print("Loading Winograd config file from ", winograd_config_dir)
+    download_public_file(full_gs_url, winograd_config_dir, True)
+    return winograd_config_dir
+
+
+def load_lower_configs():
+    from apps.stable_diffusion.src.models import get_variant_version
+    from apps.stable_diffusion.src.utils.utils import (
+        fetch_and_update_base_model_id,
+    )
+
+    base_model_id = args.hf_model_id
+    if args.ckpt_loc != "":
+        base_model_id = fetch_and_update_base_model_id(args.ckpt_loc)
+    if base_model_id == "runwayml/stable-diffusion-v1-5":
+        base_model_id = "CompVis/stable-diffusion-v1-4"
+
+    variant, version = get_variant_version(base_model_id)
+
+    config_bucket = "gs://shark_tank/sd_tuned_configs/"
+
+    device, device_spec_args = get_device_args()
+    spec = ""
+    if device_spec_args:
+        spec = device_spec_args[-1].split("=")[-1].strip()
+        if device == "vulkan":
+            spec = spec.split("-")[0]
+
+    if args.annotation_model == "vae":
+        if not spec or spec in ["rdna3", "sm_80"]:
+            config_name = (
+                f"{args.annotation_model}_{args.precision}_{device}.json"
+            )
+        else:
+            config_name = f"{args.annotation_model}_{args.precision}_{device}_{spec}.json"
+    else:
+        if not spec or spec in ["rdna3", "sm_80"]:
+            config_name = f"{args.annotation_model}_{version}_{args.precision}_{device}.json"
+        else:
+            config_name = f"{args.annotation_model}_{version}_{args.precision}_{device}_{spec}.json"
+
+    full_gs_url = config_bucket + config_name
+    lowering_config_dir = os.path.join(WORKDIR, "configs", config_name)
+    print("Loading lowering config file from ", lowering_config_dir)
+    download_public_file(full_gs_url, lowering_config_dir, True)
+    return lowering_config_dir
+
+
+# Annotate the model with Winograd attribute on selected conv ops
+def annotate_with_winograd(input_mlir, winograd_config_dir, model_name):
+    with create_context() as ctx:
+        winograd_model = model_annotation(
+            ctx,
+            input_contents=input_mlir,
+            config_path=winograd_config_dir,
+            search_op="conv",
+            winograd=True,
+        )
+
+    bytecode_stream = io.BytesIO()
+    winograd_model.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+
+    if args.save_annotation:
+        if model_name.split("_")[-1] != "tuned":
+            out_file_path = os.path.join(
+                args.annotation_output, model_name + "_tuned_torch.mlir"
+            )
+        else:
+            out_file_path = os.path.join(
+                args.annotation_output, model_name + "_torch.mlir"
+            )
+        with open(out_file_path, "w") as f:
+            f.write(str(winograd_model))
+            f.close()
+
+    return bytecode
+
+
+def dump_after_mlir(input_mlir, use_winograd):
+    import iree.compiler as ireec
+
+    device, device_spec_args = get_device_args()
+    if use_winograd:
+        preprocess_flag = "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-flow-convert-1x1-filter-conv2d-to-matmul,iree-preprocessing-convert-conv2d-to-img2col,iree-preprocessing-pad-linalg-ops{pad-size=32},iree-linalg-ext-convert-conv2d-to-winograd))"
+    else:
+        preprocess_flag = "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-flow-convert-1x1-filter-conv2d-to-matmul,iree-preprocessing-convert-conv2d-to-img2col,iree-preprocessing-pad-linalg-ops{pad-size=32}))"
+
+    dump_module = ireec.compile_str(
+        input_mlir,
+        target_backends=[iree_target_map(device)],
+        extra_args=device_spec_args
+        + [
+            preprocess_flag,
+            "--compile-to=preprocessing",
+        ],
+    )
+    return dump_module
+
+
+# For Unet annotate the model with tuned lowering configs
+def annotate_with_lower_configs(
+    input_mlir, lowering_config_dir, model_name, use_winograd
+):
+    # Dump IR after padding/img2col/winograd passes
+    dump_module = dump_after_mlir(input_mlir, use_winograd)
+    print("Applying tuned configs on", model_name)
+
+    # Annotate the model with lowering configs in the config file
+    with create_context() as ctx:
+        tuned_model = model_annotation(
+            ctx,
+            input_contents=dump_module,
+            config_path=lowering_config_dir,
+            search_op="all",
+        )
+
+    bytecode_stream = io.BytesIO()
+    tuned_model.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+
+    if args.save_annotation:
+        if model_name.split("_")[-1] != "tuned":
+            out_file_path = (
+                f"{args.annotation_output}/{model_name}_tuned_torch.mlir"
+            )
+        else:
+            out_file_path = f"{args.annotation_output}/{model_name}_torch.mlir"
+        with open(out_file_path, "w") as f:
+            f.write(str(tuned_model))
+            f.close()
+
+    return bytecode
+
+
+def sd_model_annotation(mlir_model, model_name):
+    device = get_device()
+    if args.annotation_model == "unet" and device == "vulkan":
+        use_winograd = True
+        winograd_config_dir = load_winograd_configs()
+        winograd_model = annotate_with_winograd(
+            mlir_model, winograd_config_dir, model_name
+        )
+        lowering_config_dir = load_lower_configs()
+        tuned_model = annotate_with_lower_configs(
+            winograd_model, lowering_config_dir, model_name, use_winograd
+        )
+    elif args.annotation_model == "vae" and device == "vulkan":
+        use_winograd = True
+        winograd_config_dir = load_winograd_configs()
+        tuned_model = annotate_with_winograd(
+            mlir_model, winograd_config_dir, model_name
+        )
+    else:
+        use_winograd = False
+        lowering_config_dir = load_lower_configs()
+        tuned_model = annotate_with_lower_configs(
+            mlir_model, lowering_config_dir, model_name, use_winograd
+        )
+    return tuned_model
+
+
+if __name__ == "__main__":
+    mlir_model, model_name = load_model_from_tank()
+    sd_model_annotation(mlir_model, model_name)

apps/stable_diffusion/src/utils/stable_args.py

@@ -1,4 +1,10 @@
 import argparse
+from pathlib import Path
+
+
+def path_expand(s):
+    return Path(s).expanduser().resolve()
+
 
 p = argparse.ArgumentParser(
     description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -9,6 +15,7 @@ p = argparse.ArgumentParser(
 ##############################################################################
 
 p.add_argument(
+    "-p",
     "--prompts",
     nargs="+",
     default=["cyberpunk forest by Salvador Dali"],
@@ -16,12 +23,18 @@ p.add_argument(
 )
 
 p.add_argument(
-    "--negative-prompts",
+    "--negative_prompts",
     nargs="+",
-    default=[""],
+    default=["trees, green"],
     help="text you don't want to see in the generated image.",
 )
 
+p.add_argument(
+    "--img_path",
+    type=str,
+    help="Path to the image input for img2img/inpainting",
+)
+
 p.add_argument(
     "--steps",
     type=int,
@@ -32,8 +45,30 @@ p.add_argument(
 p.add_argument(
     "--seed",
     type=int,
-    default=42,
-    help="the seed to use.",
+    default=-1,
+    help="the seed to use. -1 for a random one.",
+)
+
+p.add_argument(
+    "--batch_size",
+    type=int,
+    default=1,
+    choices=range(1, 4),
+    help="the number of inferences to be made in a single `batch_count`.",
+)
+
+p.add_argument(
+    "--height",
+    type=int,
+    default=512,
+    help="the height of the output image.",
+)
+
+p.add_argument(
+    "--width",
+    type=int,
+    default=512,
+    help="the width of the output image.",
 )
 
 p.add_argument(
@@ -50,6 +85,81 @@ p.add_argument(
     help="max length of the tokenizer output, options are 64 and 77.",
 )
 
+p.add_argument(
+    "--strength",
+    type=float,
+    default=0.8,
+    help="the strength of change applied on the given input image for img2img",
+)
+
+##############################################################################
+### Inpainting and Outpainting Params
+##############################################################################
+
+p.add_argument(
+    "--mask_path",
+    type=str,
+    help="Path to the mask image input for inpainting",
+)
+
+p.add_argument(
+    "--pixels",
+    type=int,
+    default=128,
+    choices=range(8, 256, 8),
+    help="Number of expended pixels for one direction for outpainting",
+)
+
+p.add_argument(
+    "--mask_blur",
+    type=int,
+    default=8,
+    choices=range(0, 64),
+    help="Number of blur pixels for outpainting",
+)
+
+p.add_argument(
+    "--left",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If expend left for outpainting",
+)
+
+p.add_argument(
+    "--right",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If expend right for outpainting",
+)
+
+p.add_argument(
+    "--top",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If expend top for outpainting",
+)
+
+p.add_argument(
+    "--bottom",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If expend 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
 ##############################################################################
@@ -58,13 +168,6 @@ p.add_argument(
     "--device", type=str, default="vulkan", help="device to run the model."
 )
 
-p.add_argument(
-    "--version",
-    type=str,
-    default="v2_1base",
-    help="Specify version of stable diffusion model",
-)
-
 p.add_argument(
     "--precision", type=str, default="fp16", help="precision to run the model."
 )
@@ -104,12 +207,6 @@ p.add_argument(
     help="Do conversion from the VAE output to pixel space on cpu.",
 )
 
-p.add_argument(
-    "--variant",
-    default="stablediffusion",
-    help="We now support multiple vairants of SD finetuned for different dataset. you can use the following anythingv3, ...",  # TODO add more once supported
-)
-
 p.add_argument(
     "--scheduler",
     type=str,
@@ -117,12 +214,61 @@ p.add_argument(
     help="other supported schedulers are [PNDM, DDIM, LMSDiscrete, EulerDiscrete, DPMSolverMultistep]",
 )
 
+p.add_argument(
+    "--output_img_format",
+    type=str,
+    default="png",
+    help="specify the format in which output image is save. Supported options: jpg / png",
+)
+
+p.add_argument(
+    "--output_dir",
+    type=str,
+    default=None,
+    help="Directory path to save the output images and json",
+)
+
+p.add_argument(
+    "--batch_count",
+    type=int,
+    default=1,
+    help="number of batch to be generated with random seeds in single execution",
+)
+
+p.add_argument(
+    "--ckpt_loc",
+    type=str,
+    default="",
+    help="Path to SD's .ckpt file.",
+)
+
+p.add_argument(
+    "--custom_vae",
+    type=str,
+    default="",
+    help="HuggingFace repo-id or path to SD model's checkpoint whose Vae needs to be plugged in.",
+)
+
+p.add_argument(
+    "--hf_model_id",
+    type=str,
+    default="stabilityai/stable-diffusion-2-1-base",
+    help="The repo-id of hugging face.",
+)
+
+p.add_argument(
+    "--low_cpu_mem_usage",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Use the accelerate package to reduce cpu memory consumption",
+)
+
 ##############################################################################
 ### IREE - Vulkan supported flags
 ##############################################################################
 
 p.add_argument(
-    "--iree-vulkan-target-triple",
+    "--iree_vulkan_target_triple",
     type=str,
     default="",
     help="Specify target triple for vulkan",
@@ -212,6 +358,20 @@ p.add_argument(
     help="flag to clear all mlir and vmfb from common locations. Recompiling will take several minutes",
 )
 
+p.add_argument(
+    "--save_metadata_to_json",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for whether or not to save a generation information json file with the image.",
+)
+
+p.add_argument(
+    "--write_metadata_to_png",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="flag for whether or not to save generation information in PNG chunk text to generated images.",
+)
+
 ##############################################################################
 ### Web UI flags
 ##############################################################################
@@ -220,7 +380,54 @@ p.add_argument(
     "--progress_bar",
     default=True,
     action=argparse.BooleanOptionalAction,
-    help="flag for removing the pregress bar animation during image generation",
+    help="flag for removing the progress bar animation during image generation",
 )
 
-args = p.parse_args()
+p.add_argument(
+    "--ckpt_dir",
+    type=str,
+    default="",
+    help="Path to directory where all .ckpts are stored in order to populate them in the web UI",
+)
+
+
+p.add_argument(
+    "--share",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for generating a public URL",
+)
+
+p.add_argument(
+    "--server_port",
+    type=int,
+    default=8080,
+    help="flag for setting server port",
+)
+
+##############################################################################
+### SD model auto-annotation flags
+##############################################################################
+
+p.add_argument(
+    "--annotation_output",
+    type=path_expand,
+    default="./",
+    help="Directory to save the annotated mlir file",
+)
+
+p.add_argument(
+    "--annotation_model",
+    type=str,
+    default="unet",
+    help="Options are unet and vae.",
+)
+
+p.add_argument(
+    "--save_annotation",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Save annotated mlir file",
+)
+
+args, unknown = p.parse_known_args()

apps/stable_diffusion/src/utils/utils.py

@@ -0,0 +1,612 @@
+import os
+import gc
+import json
+import re
+from PIL import PngImagePlugin
+from datetime import datetime as dt
+from csv import DictWriter
+from pathlib import Path
+import numpy as np
+from random import randint
+from shark.shark_inference import SharkInference
+from shark.shark_importer import import_with_fx
+from shark.iree_utils.vulkan_utils import (
+    set_iree_vulkan_runtime_flags,
+    get_vulkan_target_triple,
+)
+from shark.iree_utils.gpu_utils import get_cuda_sm_cc
+from apps.stable_diffusion.src.utils.stable_args import args
+from apps.stable_diffusion.src.utils.resources import opt_flags
+from apps.stable_diffusion.src.utils.sd_annotation import sd_model_annotation
+import sys
+from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
+    load_pipeline_from_original_stable_diffusion_ckpt,
+)
+
+
+def get_extended_name(model_name):
+    device = (
+        args.device
+        if "://" not in args.device
+        else "-".join(args.device.split("://"))
+    )
+    extended_name = "{}_{}".format(model_name, device)
+    return extended_name
+
+
+def get_vmfb_path_name(model_name):
+    vmfb_path = os.path.join(os.getcwd(), model_name + ".vmfb")
+    return vmfb_path
+
+
+def _compile_module(shark_module, model_name, extra_args=[]):
+    if args.load_vmfb or args.save_vmfb:
+        vmfb_path = get_vmfb_path_name(model_name)
+        if args.load_vmfb and os.path.isfile(vmfb_path) and not args.save_vmfb:
+            print(f"loading existing vmfb from: {vmfb_path}")
+            shark_module.load_module(vmfb_path, extra_args=extra_args)
+        else:
+            if args.save_vmfb:
+                print("Saving to {}".format(vmfb_path))
+            else:
+                print(
+                    "No vmfb found. Compiling and saving to {}".format(
+                        vmfb_path
+                    )
+                )
+            path = shark_module.save_module(
+                os.getcwd(), model_name, extra_args
+            )
+            shark_module.load_module(path, extra_args=extra_args)
+    else:
+        shark_module.compile(extra_args)
+    return shark_module
+
+
+# Downloads the model from shark_tank and returns the shark_module.
+def get_shark_model(tank_url, model_name, extra_args=[]):
+    from shark.parser import shark_args
+
+    # Set local shark_tank cache directory.
+    shark_args.local_tank_cache = args.local_tank_cache
+
+    from shark.shark_downloader import download_model
+
+    if "cuda" in args.device:
+        shark_args.enable_tf32 = True
+
+    mlir_model, func_name, inputs, golden_out = download_model(
+        model_name,
+        tank_url=tank_url,
+        frontend="torch",
+    )
+    shark_module = SharkInference(
+        mlir_model, device=args.device, mlir_dialect="linalg"
+    )
+    return _compile_module(shark_module, model_name, extra_args)
+
+
+# Converts the torch-module into a shark_module.
+def compile_through_fx(
+    model,
+    inputs,
+    model_name,
+    is_f16=False,
+    f16_input_mask=None,
+    use_tuned=False,
+    extra_args=[],
+):
+    from shark.parser import shark_args
+
+    if "cuda" in args.device:
+        shark_args.enable_tf32 = True
+
+    mlir_module, func_name = import_with_fx(
+        model, inputs, is_f16, f16_input_mask
+    )
+
+    if use_tuned:
+        if "vae" in model_name.split("_")[0]:
+            args.annotation_model = "vae"
+        mlir_module = sd_model_annotation(mlir_module, model_name)
+
+    shark_module = SharkInference(
+        mlir_module,
+        device=args.device,
+        mlir_dialect="linalg",
+    )
+
+    del mlir_module
+    gc.collect()
+
+    return _compile_module(shark_module, model_name, extra_args)
+
+
+def set_iree_runtime_flags():
+    vulkan_runtime_flags = [
+        f"--vulkan_large_heap_block_size={args.vulkan_large_heap_block_size}",
+        f"--vulkan_validation_layers={'true' if args.vulkan_validation_layers else 'false'}",
+    ]
+    if args.enable_rgp:
+        vulkan_runtime_flags += [
+            f"--enable_rgp=true",
+            f"--vulkan_debug_utils=true",
+        ]
+    set_iree_vulkan_runtime_flags(flags=vulkan_runtime_flags)
+
+
+def get_all_devices(driver_name):
+    """
+    Inputs: driver_name
+    Returns a list of all the available devices for a given driver sorted by
+    the iree path names of the device as in --list_devices option in iree.
+    """
+    from iree.runtime import get_driver
+
+    driver = get_driver(driver_name)
+    device_list_src = driver.query_available_devices()
+    device_list_src.sort(key=lambda d: d["path"])
+    return device_list_src
+
+
+def get_device_mapping(driver, key_combination=3):
+    """This method ensures consistent device ordering when choosing
+    specific devices for execution
+    Args:
+        driver (str): execution driver (vulkan, cuda, rocm, etc)
+        key_combination (int, optional): choice for mapping value for device name.
+        1 : path
+        2 : name
+        3 : (name, path)
+        Defaults to 3.
+    Returns:
+        dict: map to possible device names user can input mapped to desired combination of name/path.
+    """
+    from shark.iree_utils._common import iree_device_map
+
+    driver = iree_device_map(driver)
+    device_list = get_all_devices(driver)
+    device_map = dict()
+
+    def get_output_value(dev_dict):
+        if key_combination == 1:
+            return f"{driver}://{dev_dict['path']}"
+        if key_combination == 2:
+            return dev_dict["name"]
+        if key_combination == 3:
+            return (dev_dict["name"], f"{driver}://{dev_dict['path']}")
+
+    # mapping driver name to default device (driver://0)
+    device_map[f"{driver}"] = get_output_value(device_list[0])
+    for i, device in enumerate(device_list):
+        # mapping with index
+        device_map[f"{driver}://{i}"] = get_output_value(device)
+        # mapping with full path
+        device_map[f"{driver}://{device['path']}"] = get_output_value(device)
+    return device_map
+
+
+def map_device_to_name_path(device, key_combination=3):
+    """Gives the appropriate device data (supported name/path) for user selected execution device
+    Args:
+        device (str): user
+        key_combination (int, optional): choice for mapping value for device name.
+        1 : path
+        2 : name
+        3 : (name, path)
+        Defaults to 3.
+    Raises:
+        ValueError:
+    Returns:
+        str / tuple: returns the mapping str or tuple of mapping str for the device depending on key_combination value
+    """
+    driver = device.split("://")[0]
+    device_map = get_device_mapping(driver, key_combination)
+    try:
+        device_mapping = device_map[device]
+    except KeyError:
+        raise ValueError(f"Device '{device}' is not a valid device.")
+    return device_mapping
+
+
+def set_init_device_flags():
+    if "vulkan" in args.device:
+        # set runtime flags for vulkan.
+        set_iree_runtime_flags()
+
+        # set triple flag to avoid multiple calls to get_vulkan_triple_flag
+        device_name, args.device = map_device_to_name_path(args.device)
+        if not args.iree_vulkan_target_triple:
+            triple = get_vulkan_target_triple(device_name)
+            if triple is not None:
+                args.iree_vulkan_target_triple = triple
+        print(
+            f"Found device {device_name}. Using target triple {args.iree_vulkan_target_triple}."
+        )
+    elif "cuda" in args.device:
+        args.device = "cuda"
+    elif "cpu" in args.device:
+        args.device = "cpu"
+
+    # set max_length based on availability.
+    if args.hf_model_id in [
+        "Linaqruf/anything-v3.0",
+        "wavymulder/Analog-Diffusion",
+        "dreamlike-art/dreamlike-diffusion-1.0",
+    ]:
+        args.max_length = 77
+    elif args.hf_model_id == "prompthero/openjourney":
+        args.max_length = 64
+
+    # Use tuned models in the case of fp16, vulkan rdna3 or cuda sm devices.
+    base_model_id = args.hf_model_id
+    if args.ckpt_loc != "":
+        base_model_id = fetch_and_update_base_model_id(args.ckpt_loc)
+
+    if (
+        "inpainting" in args.hf_model_id
+        or args.precision != "fp16"
+        or args.height != 512
+        or args.width != 512
+        or args.batch_size != 1
+        or ("vulkan" not in args.device and "cuda" not in args.device)
+    ):
+        args.use_tuned = False
+
+    elif args.ckpt_loc != "" and base_model_id not in [
+        "Linaqruf/anything-v3.0",
+        "dreamlike-art/dreamlike-diffusion-1.0",
+        "prompthero/openjourney",
+        "wavymulder/Analog-Diffusion",
+        "stabilityai/stable-diffusion-2-1",
+        "stabilityai/stable-diffusion-2-1-base",
+        "CompVis/stable-diffusion-v1-4",
+        "runwayml/stable-diffusion-v1-5",
+    ]:
+        args.use_tuned = False
+
+    elif "vulkan" in args.device and not any(
+        x in args.iree_vulkan_target_triple for x in ["rdna2", "rdna3"]
+    ):
+        args.use_tuned = False
+
+    elif "cuda" in args.device and get_cuda_sm_cc() not in ["sm_80", "sm_89"]:
+        args.use_tuned = False
+
+    elif args.use_base_vae and args.hf_model_id not in [
+        "stabilityai/stable-diffusion-2-1-base",
+        "CompVis/stable-diffusion-v1-4",
+    ]:
+        args.use_tuned = False
+
+    if args.use_tuned:
+        print(f"Using tuned models for {base_model_id}/fp16/{args.device}.")
+    else:
+        print("Tuned models are currently not supported for this setting.")
+
+    # set import_mlir to True for unuploaded models.
+    if args.ckpt_loc != "":
+        args.import_mlir = True
+
+    elif args.hf_model_id not in [
+        "Linaqruf/anything-v3.0",
+        "dreamlike-art/dreamlike-diffusion-1.0",
+        "prompthero/openjourney",
+        "wavymulder/Analog-Diffusion",
+        "stabilityai/stable-diffusion-2-1",
+        "stabilityai/stable-diffusion-2-1-base",
+        "CompVis/stable-diffusion-v1-4",
+        "runwayml/stable-diffusion-inpainting",
+        "stabilityai/stable-diffusion-2-inpainting",
+    ]:
+        args.import_mlir = True
+
+    elif args.height != 512 or args.width != 512 or args.batch_size != 1:
+        args.import_mlir = True
+
+    elif args.use_tuned and args.hf_model_id in [
+        "dreamlike-art/dreamlike-diffusion-1.0",
+        "prompthero/openjourney",
+        "stabilityai/stable-diffusion-2-1",
+    ]:
+        args.import_mlir = True
+
+    elif (
+        args.use_tuned
+        and "vulkan" in args.device
+        and "rdna2" in args.iree_vulkan_target_triple
+    ):
+        args.import_mlir = True
+
+    elif (
+        args.use_tuned
+        and "cuda" in args.device
+        and get_cuda_sm_cc() == "sm_89"
+    ):
+        args.import_mlir = True
+
+
+# Utility to get list of devices available.
+def get_available_devices():
+    def get_devices_by_name(driver_name):
+        from shark.iree_utils._common import iree_device_map
+
+        device_list = []
+        try:
+            driver_name = iree_device_map(driver_name)
+            device_list_dict = get_all_devices(driver_name)
+            print(f"{driver_name} devices are available.")
+        except:
+            print(f"{driver_name} devices are not available.")
+        else:
+            for i, device in enumerate(device_list_dict):
+                device_list.append(f"{device['name']} => {driver_name}://{i}")
+        return device_list
+
+    set_iree_runtime_flags()
+
+    available_devices = []
+    vulkan_devices = get_devices_by_name("vulkan")
+    available_devices.extend(vulkan_devices)
+    cuda_devices = get_devices_by_name("cuda")
+    available_devices.extend(cuda_devices)
+    available_devices.append("cpu")
+    return available_devices
+
+
+def disk_space_check(path, lim=20):
+    from shutil import disk_usage
+
+    du = disk_usage(path)
+    free = du.free / (1024 * 1024 * 1024)
+    if free <= lim:
+        print(f"[WARNING] Only {free:.2f}GB space available in {path}.")
+
+
+def get_opt_flags(model, precision="fp16"):
+    iree_flags = []
+    is_tuned = "tuned" if args.use_tuned else "untuned"
+    if len(args.iree_vulkan_target_triple) > 0:
+        iree_flags.append(
+            f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+        )
+
+    # Disable bindings fusion to work with moltenVK.
+    if sys.platform == "darwin":
+        iree_flags.append("-iree-stream-fuse-binding=false")
+
+    if "default_compilation_flags" in opt_flags[model][is_tuned][precision]:
+        iree_flags += opt_flags[model][is_tuned][precision][
+            "default_compilation_flags"
+        ]
+
+    if "specified_compilation_flags" in opt_flags[model][is_tuned][precision]:
+        device = (
+            args.device
+            if "://" not in args.device
+            else args.device.split("://")[0]
+        )
+        if (
+            device
+            not in opt_flags[model][is_tuned][precision][
+                "specified_compilation_flags"
+            ]
+        ):
+            device = "default_device"
+        iree_flags += opt_flags[model][is_tuned][precision][
+            "specified_compilation_flags"
+        ][device]
+    return iree_flags
+
+
+def get_path_stem(path):
+    path = Path(path)
+    return path.stem
+
+
+def get_path_to_diffusers_checkpoint(custom_weights):
+    path = Path(custom_weights)
+    diffusers_path = path.parent.absolute()
+    diffusers_directory_name = path.stem
+    complete_path_to_diffusers = diffusers_path / diffusers_directory_name
+    complete_path_to_diffusers.mkdir(parents=True, exist_ok=True)
+    path_to_diffusers = complete_path_to_diffusers.as_posix()
+    return path_to_diffusers
+
+
+def preprocessCKPT(custom_weights):
+    path_to_diffusers = get_path_to_diffusers_checkpoint(custom_weights)
+    if next(Path(path_to_diffusers).iterdir(), None):
+        print("Checkpoint already loaded at : ", path_to_diffusers)
+        return
+    else:
+        print(
+            "Diffusers' checkpoint will be identified here : ",
+            path_to_diffusers,
+        )
+    from_safetensors = (
+        True if custom_weights.lower().endswith(".safetensors") else False
+    )
+    # EMA weights usually yield higher quality images for inference but non-EMA weights have
+    # been yielding better results in our case.
+    # TODO: Add an option `--ema` (`--no-ema`) for users to specify if they want to go for EMA
+    #       weight extraction or not.
+    extract_ema = False
+    print(
+        "Loading diffusers' pipeline from original stable diffusion checkpoint"
+    )
+    pipe = load_pipeline_from_original_stable_diffusion_ckpt(
+        checkpoint_path=custom_weights,
+        extract_ema=extract_ema,
+        from_safetensors=from_safetensors,
+    )
+    pipe.save_pretrained(path_to_diffusers)
+    print("Loading complete")
+
+
+def load_vmfb(vmfb_path, model, precision):
+    model = "vae" if "base_vae" in model or "vae_encode" in model else model
+    precision = "fp32" if "clip" in model else precision
+    extra_args = get_opt_flags(model, precision)
+    shark_module = SharkInference(mlir_module=None, device=args.device)
+    shark_module.load_module(vmfb_path, extra_args=extra_args)
+    return shark_module
+
+
+# This utility returns vmfbs of Clip, Unet, Vae and Vae_encode, in case all of them
+# are present; deletes them otherwise.
+def fetch_or_delete_vmfbs(
+    extended_model_name, need_vae_encode, precision="fp32"
+):
+    vmfb_path = [
+        get_vmfb_path_name(extended_model_name[model])
+        for model in extended_model_name
+    ]
+    vmfb_present = [os.path.isfile(vmfb) for vmfb in vmfb_path]
+    all_vmfb_present = True
+    compiled_models = []
+    for i in range(3):
+        all_vmfb_present = all_vmfb_present and vmfb_present[i]
+        compiled_models.append(None)
+    if need_vae_encode:
+        all_vmfb_present = all_vmfb_present and vmfb_present[3]
+        compiled_models.append(None)
+
+    # We need to delete vmfbs only if some of the models were compiled.
+    if not all_vmfb_present:
+        for i in range(len(compiled_models)):
+            if vmfb_present[i]:
+                os.remove(vmfb_path[i])
+                print("Deleted: ", vmfb_path[i])
+    else:
+        model_name = [model for model in extended_model_name.keys()]
+        for i in range(len(compiled_models)):
+            compiled_models[i] = load_vmfb(
+                vmfb_path[i], model_name[i], precision
+            )
+    return compiled_models
+
+
+# `fetch_and_update_base_model_id` is a resource utility function which
+# helps maintaining mapping of the model to run with its base model.
+# If `base_model` is "", then this function tries to fetch the base model
+# info for the `model_to_run`.
+def fetch_and_update_base_model_id(model_to_run, base_model=""):
+    variants_path = os.path.join(os.getcwd(), "variants.json")
+    data = {model_to_run: base_model}
+    json_data = {}
+    if os.path.exists(variants_path):
+        with open(variants_path, "r", encoding="utf-8") as jsonFile:
+            json_data = json.load(jsonFile)
+            # Return with base_model's info if base_model is "".
+            if base_model == "":
+                if model_to_run in json_data:
+                    base_model = json_data[model_to_run]
+                return base_model
+    elif base_model == "":
+        return base_model
+    # Update JSON data to contain an entry mapping model_to_run with base_model.
+    json_data.update(data)
+    with open(variants_path, "w", encoding="utf-8") as jsonFile:
+        json.dump(json_data, jsonFile)
+
+
+# Generate and return a new seed if the provided one is not in the supported range (including -1)
+def sanitize_seed(seed):
+    uint32_info = np.iinfo(np.uint32)
+    uint32_min, uint32_max = uint32_info.min, uint32_info.max
+    if seed < uint32_min or seed >= uint32_max:
+        seed = randint(uint32_min, uint32_max)
+    return seed
+
+
+# clear all the cached objects to recompile cleanly.
+def clear_all():
+    print("CLEARING ALL, EXPECT SEVERAL MINUTES TO RECOMPILE")
+    from glob import glob
+    import shutil
+
+    vmfbs = glob(os.path.join(os.getcwd(), "*.vmfb"))
+    for vmfb in vmfbs:
+        if os.path.exists(vmfb):
+            os.remove(vmfb)
+    # Temporary workaround of deleting yaml files to incorporate diffusers' pipeline.
+    # TODO: Remove this once we have better weight updation logic.
+    inference_yaml = ["v2-inference-v.yaml", "v1-inference.yaml"]
+    for yaml in inference_yaml:
+        if os.path.exists(yaml):
+            os.remove(yaml)
+    home = os.path.expanduser("~")
+    if os.name == "nt":  # Windows
+        appdata = os.getenv("LOCALAPPDATA")
+        shutil.rmtree(os.path.join(appdata, "AMD/VkCache"), ignore_errors=True)
+        shutil.rmtree(os.path.join(home, "shark_tank"), ignore_errors=True)
+    elif os.name == "unix":
+        shutil.rmtree(os.path.join(home, ".cache/AMD/VkCache"))
+        shutil.rmtree(os.path.join(home, ".local/shark_tank"))
+
+
+# save output images and the inputs corresponding to it.
+def save_output_img(output_img, img_seed):
+    output_path = args.output_dir if args.output_dir else Path.cwd()
+    generated_imgs_path = Path(
+        output_path, "generated_imgs", dt.now().strftime("%Y%m%d")
+    )
+    generated_imgs_path.mkdir(parents=True, exist_ok=True)
+    csv_path = Path(generated_imgs_path, "imgs_details.csv")
+
+    prompt_slice = re.sub("[^a-zA-Z0-9]", "_", args.prompts[0][:15])
+    out_img_name = (
+        f"{prompt_slice}_{img_seed}_{dt.now().strftime('%y%m%d_%H%M%S')}"
+    )
+
+    img_model = args.hf_model_id
+    if args.ckpt_loc:
+        img_model = os.path.basename(args.ckpt_loc)
+
+    if args.output_img_format == "jpg":
+        out_img_path = Path(generated_imgs_path, f"{out_img_name}.jpg")
+        output_img.save(out_img_path, quality=95, subsampling=0)
+    else:
+        out_img_path = Path(generated_imgs_path, f"{out_img_name}.png")
+        pngInfo = PngImagePlugin.PngInfo()
+
+        if args.write_metadata_to_png:
+            pngInfo.add_text(
+                "parameters",
+                f"{args.prompts[0]}\nNegative prompt: {args.negative_prompts[0]}\nSteps:{args.steps}, Sampler: {args.scheduler}, CFG scale: {args.guidance_scale}, Seed: {img_seed}, Size: {args.width}x{args.height}, Model: {img_model}",
+            )
+
+        output_img.save(out_img_path, "PNG", pnginfo=pngInfo)
+
+        if args.output_img_format not in ["png", "jpg"]:
+            print(
+                f"[ERROR] Format {args.output_img_format} is not supported yet."
+                "Image saved as png instead. Supported formats: png / jpg"
+            )
+
+    new_entry = {
+        "VARIANT": img_model,
+        "SCHEDULER": args.scheduler,
+        "PROMPT": args.prompts[0],
+        "NEG_PROMPT": args.negative_prompts[0],
+        "SEED": img_seed,
+        "CFG_SCALE": args.guidance_scale,
+        "PRECISION": args.precision,
+        "STEPS": args.steps,
+        "HEIGHT": args.height,
+        "WIDTH": args.width,
+        "MAX_LENGTH": args.max_length,
+        "OUTPUT": out_img_path,
+    }
+
+    with open(csv_path, "a") as csv_obj:
+        dictwriter_obj = DictWriter(csv_obj, fieldnames=list(new_entry.keys()))
+        dictwriter_obj.writerow(new_entry)
+        csv_obj.close()
+
+    if args.save_metadata_to_json:
+        del new_entry["OUTPUT"]
+        json_path = Path(generated_imgs_path, f"{out_img_name}.json")
+        with open(json_path, "w") as f:
+            json.dump(new_entry, f, indent=4)

apps/stable_diffusion/stable_diffusion_telegram_bot.md

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

apps/stable_diffusion/web/index.py

@@ -0,0 +1,49 @@
+import os
+import sys
+
+if sys.platform == "darwin":
+    os.environ["DYLD_LIBRARY_PATH"] = "/usr/local/lib"
+
+import gradio as gr
+from apps.stable_diffusion.src import args, clear_all
+from apps.stable_diffusion.web.utils.gradio_configs import (
+    clear_gradio_tmp_imgs_folder,
+)
+
+# clear all gradio tmp images from the last session
+clear_gradio_tmp_imgs_folder()
+
+if args.clear_all:
+    clear_all()
+
+
+def resource_path(relative_path):
+    """Get absolute path to resource, works for dev and for PyInstaller"""
+    base_path = getattr(
+        sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__))
+    )
+    return os.path.join(base_path, relative_path)
+
+
+dark_theme = resource_path("ui/css/sd_dark_theme.css")
+
+from apps.stable_diffusion.web.ui import (
+    txt2img_web,
+    img2img_web,
+    inpaint_web,
+    outpaint_web,
+)
+
+sd_web = gr.TabbedInterface(
+    [txt2img_web, img2img_web, inpaint_web, outpaint_web],
+    ["Text-to-Image", "Image-to-Image", "Inpainting", "Outpainting"],
+    css=dark_theme,
+)
+
+sd_web.queue()
+sd_web.launch(
+    share=args.share,
+    inbrowser=True,
+    server_name="0.0.0.0",
+    server_port=args.server_port,
+)

apps/stable_diffusion/web/ui/__init__.py

@@ -0,0 +1,4 @@
+from apps.stable_diffusion.web.ui.txt2img_ui import txt2img_web
+from apps.stable_diffusion.web.ui.img2img_ui import img2img_web
+from apps.stable_diffusion.web.ui.inpaint_ui import inpaint_web
+from apps.stable_diffusion.web.ui.outpaint_ui import outpaint_web

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

@@ -0,0 +1,215 @@
+
+/* Overwrite the Gradio default theme with their .dark theme declarations */
+
+:root {
+    --color-focus-primary: var(--color-grey-700);
+    --color-focus-secondary: var(--color-grey-600);
+    --color-focus-ring: rgb(55 65 81);
+    --color-background-primary: var(--color-grey-950);
+    --color-background-secondary: var(--color-grey-900);
+    --color-background-tertiary: var(--color-grey-800);
+    --color-text-body: var(--color-grey-100);
+    --color-text-label: var(--color-grey-200);
+    --color-text-placeholder: var(--color-grey);
+    --color-text-subdued: var(--color-grey-400);
+    --color-text-link-base: var(--color-blue-500);
+    --color-text-link-hover: var(--color-blue-400);
+    --color-text-link-visited: var(--color-blue-600);
+    --color-text-link-active: var(--color-blue-500);
+    --color-text-code-background: var(--color-grey-800);
+    --color-text-code-border: color.border-primary;
+    --color-border-primary: var(--color-grey-700);
+    --color-border-secondary: var(--color-grey-600);
+    --color-border-highlight: var(--color-accent-base);
+    --color-accent-base: var(--color-orange-500);
+    --color-accent-light: var(--color-orange-300);
+    --color-accent-dark: var(--color-orange-700);
+    --color-functional-error-base: var(--color-red-400);
+    --color-functional-error-subdued: var(--color-red-300);
+    --color-functional-error-background: var(--color-background-primary);
+    --color-functional-info-base: var(--color-yellow);
+    --color-functional-info-subdued: var(--color-yellow-300);
+    --color-functional-success-base: var(--color-green);
+    --color-functional-success-subdued: var(--color-green-300);
+    --shadow-spread: 2px;
+    --api-background: linear-gradient(to bottom, rgba(255, 216, 180, .05), transparent);
+    --api-pill-background: var(--color-orange-400);
+    --api-pill-border: var(--color-orange-600);
+    --api-pill-text: var(--color-orange-900);
+    --block-border-color: var(--color-border-primary);
+    --block-background: var(--color-background-tertiary);
+    --uploadable-border-color-hover: var(--color-border-primary);
+    --uploadable-border-color-loaded: var(--color-functional-success);
+    --uploadable-text-color: var(--color-text-subdued);
+    --block_label-border-color: var(--color-border-primary);
+    --block_label-icon-color: var(--color-text-label);
+    --block_label-shadow: var(--shadow-drop);
+    --block_label-background: var(--color-background-secondary);
+    --icon_button-icon-color-base: var(--color-text-label);
+    --icon_button-icon-color-hover: var(--color-text-label);
+    --icon_button-background-base: var(--color-background-primary);
+    --icon_button-background-hover: var(--color-background-primary);
+    --icon_button-border-color-base: var(--color-background-primary);
+    --icon_button-border-color-hover: var(--color-border-secondary);
+    --input-text-color: var(--color-text-body);
+    --input-border-color-base: var(--color-border-primary);
+    --input-border-color-hover: var(--color-border-primary);
+    --input-border-color-focus: var(--color-border-primary);
+    --input-background-base: var(--color-background-tertiary);
+    --input-background-hover: var(--color-background-tertiary);
+    --input-background-focus: var(--color-background-tertiary);
+    --input-shadow: var(--shadow-inset);
+    --checkbox-border-color-base: var(--color-border-primary);
+    --checkbox-border-color-hover: var(--color-focus-primary);
+    --checkbox-border-color-focus: var(--color-blue-500);
+    --checkbox-background-base: var(--color-background-primary);
+    --checkbox-background-hover: var(--color-background-primary);
+    --checkbox-background-focus: var(--color-background-primary);
+    --checkbox-background-selected: var(--color-blue-600);
+    --checkbox-label-border-color-base: var(--color-border-primary);
+    --checkbox-label-border-color-hover: var(--color-border-primary);
+    --checkbox-label-border-color-focus: var(--color-border-secondary);
+    --checkbox-label-background-base: linear-gradient(to top, var(--color-grey-900), var(--color-grey-800));
+    --checkbox-label-background-hover: linear-gradient(to top, var(--color-grey-900), var(--color-grey-800));
+    --checkbox-label-background-focus: linear-gradient(to top, var(--color-grey-900), var(--color-grey-800));
+    --form-seperator-color: var(--color-border-primary);
+    --button-primary-border-color-base: var(--color-orange-600);
+    --button-primary-border-color-hover: var(--color-orange-600);
+    --button-primary-border-color-focus: var(--color-orange-600);
+    --button-primary-text-color-base: white;
+    --button-primary-text-color-hover: white;
+    --button-primary-text-color-focus: white;
+    --button-primary-background-base: linear-gradient(to bottom right, var(--color-orange-700), var(--color-orange-700));
+    --button-primary-background-hover: linear-gradient(to bottom right, var(--color-orange-700), var(--color-orange-500));
+    --button-primary-background-focus: linear-gradient(to bottom right, var(--color-orange-700), var(--color-orange-500));
+    --button-secondary-border-color-base: var(--color-grey-600);
+    --button-secondary-border-color-hover: var(--color-grey-600);
+    --button-secondary-border-color-focus: var(--color-grey-600);
+    --button-secondary-text-color-base: white;
+    --button-secondary-text-color-hover: white;
+    --button-secondary-text-color-focus: white;
+    --button-secondary-background-base: linear-gradient(to bottom right, var(--color-grey-600), var(--color-grey-700));
+    --button-secondary-background-hover: linear-gradient(to bottom right, var(--color-grey-600), var(--color-grey-600));
+    --button-secondary-background-focus: linear-gradient(to bottom right, var(--color-grey-600), var(--color-grey-600));
+    --button-cancel-border-color-base: var(--color-red-600);
+    --button-cancel-border-color-hover: var(--color-red-600);
+    --button-cancel-border-color-focus: var(--color-red-600);
+    --button-cancel-text-color-base: white;
+    --button-cancel-text-color-hover: white;
+    --button-cancel-text-color-focus: white;
+    --button-cancel-background-base: linear-gradient(to bottom right, var(--color-red-700), var(--color-red-700));
+    --button-cancel-background-focus: linear-gradient(to bottom right, var(--color-red-700), var(--color-red-500));
+    --button-cancel-background-hover: linear-gradient(to bottom right, var(--color-red-700), var(--color-red-500));
+    --button-plain-border-color-base: var(--color-grey-600);
+    --button-plain-border-color-hover: var(--color-grey-500);
+    --button-plain-border-color-focus: var(--color-grey-500);
+    --button-plain-text-color-base: var(--color-text-body);
+    --button-plain-text-color-hover: var(--color-text-body);
+    --button-plain-text-color-focus: var(--color-text-body);
+    --button-plain-background-base: var(--color-grey-700);
+    --button-plain-background-hover: var(--color-grey-700);
+    --button-plain-background-focus: var(--color-grey-700);
+    --gallery-label-background-base: var(--color-grey-50);
+    --gallery-label-background-hover: var(--color-grey-50);
+    --gallery-label-border-color-base: var(--color-border-primary);
+    --gallery-label-border-color-hover: var(--color-border-primary);
+    --gallery-thumb-background-base: var(--color-grey-900);
+    --gallery-thumb-background-hover: var(--color-grey-900);
+    --gallery-thumb-border-color-base: var(--color-border-primary);
+    --gallery-thumb-border-color-hover: var(--color-accent-base);
+    --gallery-thumb-border-color-focus: var(--color-blue-500);
+    --gallery-thumb-border-color-selected: var(--color-accent-base);
+    --chatbot-border-border-color-base: transparent;
+    --chatbot-border-border-color-latest: transparent;
+    --chatbot-user-background-base: ;
+    --chatbot-user-background-latest: ;
+    --chatbot-user-text-color-base: white;
+    --chatbot-user-text-color-latest: white;
+    --chatbot-bot-background-base: ;
+    --chatbot-bot-background-latest: ;
+    --chatbot-bot-text-color-base: white;
+    --chatbot-bot-text-color-latest: white;
+    --label-gradient-from: var(--color-orange-400);
+    --label-gradient-to: var(--color-orange-600);
+    --table-odd-background: var(--color-grey-900);
+    --table-even-background: var(--color-grey-950);
+    --table-background-edit: transparent;
+    --dataset-gallery-background-base: var(--color-background-primary);
+    --dataset-gallery-background-hover: var(--color-grey-800);
+    --dataset-dataframe-border-base: var(--color-border-primary);
+    --dataset-dataframe-border-hover: var(--color-border-secondary);
+    --dataset-table-background-base: transparent;
+    --dataset-table-background-hover: var(--color-grey-700);
+    --dataset-table-border-base: var(--color-grey-800);
+    --dataset-table-border-hover: var(--color-grey-800);
+}
+
+/* SHARK theme */
+body {
+    background-color: var(--color-background-primary);
+}
+
+/* display in full width for desktop devices */
+@media (min-width: 1536px)
+{
+    .gradio-container {
+        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;
+}
+
+#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;
+}
+
+#prompt_box_outer div:first-child {
+    border-radius: 0 !important
+}
+
+#prompt_box textarea, #negative_prompt_box textarea {
+    background-color: var(--color-background-primary) !important;
+}
+
+#prompt_examples {
+    margin: 0 !important;
+}
+
+#prompt_examples svg {
+    display: none !important;
+}
+
+#ui_body {
+    background-color: var(--color-background-secondary) !important;
+    padding: var(--size-2) !important;
+    border-radius: 0.5em !important;
+}
+
+#img_result+div {
+    display: none !important;
+}
+
+footer {
+    display: none !important;
+}
+
+#gallery + div {
+    border-radius: 0 !important;
+}

apps/stable_diffusion/web/ui/img2img_ui.py

@@ -0,0 +1,231 @@
+import os
+import sys
+import glob
+from pathlib import Path
+import gradio as gr
+from PIL import Image
+from apps.stable_diffusion.scripts import img2img_inf
+from apps.stable_diffusion.src import args
+from apps.stable_diffusion.web.ui.utils import (
+    available_devices,
+    nodlogo_loc,
+)
+
+
+with gr.Blocks(title="Image-to-Image") as img2img_web:
+    with gr.Row(elem_id="ui_title"):
+        nod_logo = Image.open(nodlogo_loc)
+        with gr.Row():
+            with gr.Column(scale=1, elem_id="demo_title_outer"):
+                gr.Image(
+                    value=nod_logo,
+                    show_label=False,
+                    interactive=False,
+                    elem_id="top_logo",
+                ).style(width=150, height=50)
+    with gr.Row(elem_id="ui_body"):
+        with gr.Row():
+            with gr.Column(scale=1, min_width=600):
+                with gr.Row():
+                    ckpt_path = (
+                        Path(args.ckpt_dir)
+                        if args.ckpt_dir
+                        else Path(Path.cwd(), "models")
+                    )
+                    ckpt_path.mkdir(parents=True, exist_ok=True)
+                    types = (
+                        "*.ckpt",
+                        "*.safetensors",
+                    )  # the tuple of file types
+                    ckpt_files = ["None"]
+                    for extn in types:
+                        files = glob.glob(os.path.join(ckpt_path, extn))
+                        ckpt_files.extend(files)
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {ckpt_path})",
+                        value=args.ckpt_loc if args.ckpt_loc else "None",
+                        choices=ckpt_files
+                        + [
+                            "Linaqruf/anything-v3.0",
+                            "prompthero/openjourney",
+                            "wavymulder/Analog-Diffusion",
+                            "stabilityai/stable-diffusion-2-1",
+                            "stabilityai/stable-diffusion-2-1-base",
+                            "CompVis/stable-diffusion-v1-4",
+                        ],
+                    )
+                    hf_model_id = gr.Textbox(
+                        placeholder="Select 'None' in the Models dropdown on the left and enter model ID here e.g: SG161222/Realistic_Vision_V1.3",
+                        value="",
+                        label="HuggingFace Model ID",
+                        lines=3,
+                    )
+
+                with gr.Group(elem_id="prompt_box_outer"):
+                    prompt = gr.Textbox(
+                        label="Prompt",
+                        value=args.prompts[0],
+                        lines=1,
+                        elem_id="prompt_box",
+                    )
+                    negative_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        value=args.negative_prompts[0],
+                        lines=1,
+                        elem_id="negative_prompt_box",
+                    )
+
+                init_image = gr.Image(label="Input Image", type="filepath")
+
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            label="Scheduler",
+                            value="PNDM",
+                            choices=[
+                                "DDIM",
+                                "PNDM",
+                                "DPMSolverMultistep",
+                                "EulerAncestralDiscrete",
+                            ],
+                        )
+                        with gr.Group():
+                            save_metadata_to_png = gr.Checkbox(
+                                label="Save prompt information to PNG",
+                                value=args.write_metadata_to_png,
+                                interactive=True,
+                            )
+                            save_metadata_to_json = gr.Checkbox(
+                                label="Save prompt information to JSON file",
+                                value=args.save_metadata_to_json,
+                                interactive=True,
+                            )
+                    with gr.Row():
+                        height = gr.Slider(
+                            384, 786, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 786, value=args.width, step=8, label="Width"
+                        )
+                        precision = gr.Radio(
+                            label="Precision",
+                            value=args.precision,
+                            choices=[
+                                "fp16",
+                                "fp32",
+                            ],
+                            visible=False,
+                        )
+                        max_length = gr.Radio(
+                            label="Max Length",
+                            value=args.max_length,
+                            choices=[
+                                64,
+                                77,
+                            ],
+                            visible=False,
+                        )
+                    with gr.Row():
+                        steps = gr.Slider(
+                            1, 100, value=args.steps, step=1, label="Steps"
+                        )
+                        strength = gr.Slider(
+                            0,
+                            1,
+                            value=args.strength,
+                            step=0.1,
+                            label="Strength",
+                        )
+                    with gr.Row():
+                        guidance_scale = gr.Slider(
+                            0,
+                            50,
+                            value=args.guidance_scale,
+                            step=0.1,
+                            label="CFG Scale",
+                        )
+                        batch_count = gr.Slider(
+                            1,
+                            100,
+                            value=args.batch_count,
+                            step=1,
+                            label="Batch Count",
+                            interactive=True,
+                        )
+                        batch_size = gr.Slider(
+                            1,
+                            4,
+                            value=args.batch_size,
+                            step=1,
+                            label="Batch Size",
+                            interactive=False,
+                            visible=False,
+                        )
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    random_seed = gr.Button("Randomize Seed")
+                    random_seed.click(
+                        None,
+                        inputs=[],
+                        outputs=[seed],
+                        _js="() => Math.floor(Math.random() * 4294967295)",
+                    )
+                    stable_diffusion = gr.Button("Generate Image(s)")
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    gallery = gr.Gallery(
+                        label="Generated images",
+                        show_label=False,
+                        elem_id="gallery",
+                    ).style(grid=[2])
+                    std_output = gr.Textbox(
+                        value="Nothing to show.",
+                        lines=1,
+                        show_label=False,
+                    )
+                output_dir = args.output_dir if args.output_dir else Path.cwd()
+                output_dir = Path(output_dir, "generated_imgs")
+                output_loc = gr.Textbox(
+                    label="Saving Images at",
+                    value=output_dir,
+                    interactive=False,
+                )
+        kwargs = dict(
+            fn=img2img_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                init_image,
+                height,
+                width,
+                steps,
+                strength,
+                guidance_scale,
+                seed,
+                batch_count,
+                batch_size,
+                scheduler,
+                custom_model,
+                hf_model_id,
+                precision,
+                device,
+                max_length,
+                save_metadata_to_json,
+                save_metadata_to_png,
+            ],
+            outputs=[gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt.submit(**kwargs)
+        negative_prompt.submit(**kwargs)
+        stable_diffusion.click(**kwargs)

apps/stable_diffusion/web/ui/inpaint_ui.py

@@ -0,0 +1,224 @@
+import os
+import sys
+import glob
+from pathlib import Path
+import gradio as gr
+from PIL import Image
+from apps.stable_diffusion.scripts import inpaint_inf
+from apps.stable_diffusion.src import args
+from apps.stable_diffusion.web.ui.utils import (
+    available_devices,
+    nodlogo_loc,
+)
+
+
+with gr.Blocks(title="Inpainting") as inpaint_web:
+    with gr.Row(elem_id="ui_title"):
+        nod_logo = Image.open(nodlogo_loc)
+        with gr.Row():
+            with gr.Column(scale=1, elem_id="demo_title_outer"):
+                gr.Image(
+                    value=nod_logo,
+                    show_label=False,
+                    interactive=False,
+                    elem_id="top_logo",
+                ).style(width=150, height=50)
+    with gr.Row(elem_id="ui_body"):
+        with gr.Row():
+            with gr.Column(scale=1, min_width=600):
+                with gr.Row():
+                    ckpt_path = (
+                        Path(args.ckpt_dir)
+                        if args.ckpt_dir
+                        else Path(Path.cwd(), "models")
+                    )
+                    ckpt_path.mkdir(parents=True, exist_ok=True)
+                    types = (
+                        "*.ckpt",
+                        "*.safetensors",
+                    )  # the tuple of file types
+                    ckpt_files = ["None"]
+                    for extn in types:
+                        files = glob.glob(os.path.join(ckpt_path, extn))
+                        ckpt_files.extend(files)
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {ckpt_path})",
+                        value=args.ckpt_loc if args.ckpt_loc else "None",
+                        choices=ckpt_files
+                        + [
+                            "runwayml/stable-diffusion-inpainting",
+                            "stabilityai/stable-diffusion-2-inpainting",
+                        ],
+                    )
+                    hf_model_id = gr.Textbox(
+                        placeholder="Select 'None' in the Models dropdown on the left and enter model ID here e.g: SG161222/Realistic_Vision_V1.3",
+                        value="",
+                        label="HuggingFace Model ID",
+                        lines=3,
+                    )
+
+                with gr.Group(elem_id="prompt_box_outer"):
+                    prompt = gr.Textbox(
+                        label="Prompt",
+                        value=args.prompts[0],
+                        lines=1,
+                        elem_id="prompt_box",
+                    )
+                    negative_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        value=args.negative_prompts[0],
+                        lines=1,
+                        elem_id="negative_prompt_box",
+                    )
+
+                init_image = gr.Image(
+                    label="Masked Image",
+                    source="upload",
+                    tool="sketch",
+                    type="filepath",
+                )
+
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            label="Scheduler",
+                            value="PNDM",
+                            choices=[
+                                "DDIM",
+                                "PNDM",
+                                "DPMSolverMultistep",
+                                "EulerAncestralDiscrete",
+                            ],
+                        )
+                        with gr.Group():
+                            save_metadata_to_png = gr.Checkbox(
+                                label="Save prompt information to PNG",
+                                value=args.write_metadata_to_png,
+                                interactive=True,
+                            )
+                            save_metadata_to_json = gr.Checkbox(
+                                label="Save prompt information to JSON file",
+                                value=args.save_metadata_to_json,
+                                interactive=True,
+                            )
+                    with gr.Row():
+                        height = gr.Slider(
+                            384, 786, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 786, value=args.width, step=8, label="Width"
+                        )
+                        precision = gr.Radio(
+                            label="Precision",
+                            value=args.precision,
+                            choices=[
+                                "fp16",
+                                "fp32",
+                            ],
+                            visible=False,
+                        )
+                        max_length = gr.Radio(
+                            label="Max Length",
+                            value=args.max_length,
+                            choices=[
+                                64,
+                                77,
+                            ],
+                            visible=False,
+                        )
+                    with gr.Row():
+                        steps = gr.Slider(
+                            1, 100, value=args.steps, step=1, label="Steps"
+                        )
+                    with gr.Row():
+                        guidance_scale = gr.Slider(
+                            0,
+                            50,
+                            value=args.guidance_scale,
+                            step=0.1,
+                            label="CFG Scale",
+                        )
+                        batch_count = gr.Slider(
+                            1,
+                            100,
+                            value=args.batch_count,
+                            step=1,
+                            label="Batch Count",
+                            interactive=True,
+                        )
+                        batch_size = gr.Slider(
+                            1,
+                            4,
+                            value=args.batch_size,
+                            step=1,
+                            label="Batch Size",
+                            interactive=False,
+                            visible=False,
+                        )
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    random_seed = gr.Button("Randomize Seed")
+                    random_seed.click(
+                        None,
+                        inputs=[],
+                        outputs=[seed],
+                        _js="() => Math.floor(Math.random() * 4294967295)",
+                    )
+                    stable_diffusion = gr.Button("Generate Image(s)")
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    gallery = gr.Gallery(
+                        label="Generated images",
+                        show_label=False,
+                        elem_id="gallery",
+                    ).style(grid=[2])
+                    std_output = gr.Textbox(
+                        value="Nothing to show.",
+                        lines=1,
+                        show_label=False,
+                    )
+                output_dir = args.output_dir if args.output_dir else Path.cwd()
+                output_dir = Path(output_dir, "generated_imgs")
+                output_loc = gr.Textbox(
+                    label="Saving Images at",
+                    value=output_dir,
+                    interactive=False,
+                )
+        kwargs = dict(
+            fn=inpaint_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                init_image,
+                height,
+                width,
+                steps,
+                guidance_scale,
+                seed,
+                batch_count,
+                batch_size,
+                scheduler,
+                custom_model,
+                hf_model_id,
+                precision,
+                device,
+                max_length,
+                save_metadata_to_json,
+                save_metadata_to_png,
+            ],
+            outputs=[gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt.submit(**kwargs)
+        negative_prompt.submit(**kwargs)
+        stable_diffusion.click(**kwargs)

apps/stable_diffusion/web/ui/outpaint_ui.py

@@ -0,0 +1,260 @@
+import os
+import sys
+import glob
+from pathlib import Path
+import gradio as gr
+from PIL import Image
+from apps.stable_diffusion.scripts import outpaint_inf
+from apps.stable_diffusion.src import args
+from apps.stable_diffusion.web.ui.utils import (
+    available_devices,
+    nodlogo_loc,
+)
+
+
+with gr.Blocks(title="Outpainting") as outpaint_web:
+    with gr.Row(elem_id="ui_title"):
+        nod_logo = Image.open(nodlogo_loc)
+        with gr.Row():
+            with gr.Column(scale=1, elem_id="demo_title_outer"):
+                gr.Image(
+                    value=nod_logo,
+                    show_label=False,
+                    interactive=False,
+                    elem_id="top_logo",
+                ).style(width=150, height=50)
+    with gr.Row(elem_id="ui_body"):
+        with gr.Row():
+            with gr.Column(scale=1, min_width=600):
+                with gr.Row():
+                    ckpt_path = (
+                        Path(args.ckpt_dir)
+                        if args.ckpt_dir
+                        else Path(Path.cwd(), "models")
+                    )
+                    ckpt_path.mkdir(parents=True, exist_ok=True)
+                    types = (
+                        "*.ckpt",
+                        "*.safetensors",
+                    )  # the tuple of file types
+                    ckpt_files = ["None"]
+                    for extn in types:
+                        files = glob.glob(os.path.join(ckpt_path, extn))
+                        ckpt_files.extend(files)
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {ckpt_path})",
+                        value=args.ckpt_loc if args.ckpt_loc else "None",
+                        choices=ckpt_files
+                        + [
+                            "runwayml/stable-diffusion-inpainting",
+                            "stabilityai/stable-diffusion-2-inpainting",
+                        ],
+                    )
+                    hf_model_id = gr.Textbox(
+                        placeholder="Select 'None' in the Models dropdown on the left and enter model ID here e.g: SG161222/Realistic_Vision_V1.3",
+                        value="",
+                        label="HuggingFace Model ID",
+                        lines=3,
+                    )
+
+                with gr.Group(elem_id="prompt_box_outer"):
+                    prompt = gr.Textbox(
+                        label="Prompt",
+                        value=args.prompts[0],
+                        lines=1,
+                        elem_id="prompt_box",
+                    )
+                    negative_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        value=args.negative_prompts[0],
+                        lines=1,
+                        elem_id="negative_prompt_box",
+                    )
+
+                init_image = gr.Image(label="Input Image", type="filepath")
+
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            label="Scheduler",
+                            value="PNDM",
+                            choices=[
+                                "DDIM",
+                                "PNDM",
+                                "DPMSolverMultistep",
+                                "EulerAncestralDiscrete",
+                            ],
+                        )
+                        with gr.Group():
+                            save_metadata_to_png = gr.Checkbox(
+                                label="Save prompt information to PNG",
+                                value=args.write_metadata_to_png,
+                                interactive=True,
+                            )
+                            save_metadata_to_json = gr.Checkbox(
+                                label="Save prompt information to JSON file",
+                                value=args.save_metadata_to_json,
+                                interactive=True,
+                            )
+                    with gr.Row():
+                        pixels = gr.Slider(
+                            8,
+                            256,
+                            value=args.pixels,
+                            step=8,
+                            label="Pixels to expand",
+                        )
+                        mask_blur = gr.Slider(
+                            0,
+                            64,
+                            value=args.mask_blur,
+                            step=1,
+                            label="Mask blur",
+                        )
+                    with gr.Row():
+                        directions = gr.CheckboxGroup(
+                            label="Outpainting direction",
+                            choices=["left", "right", "up", "down"],
+                            value=["left", "right", "up", "down"],
+                        )
+                    with gr.Row():
+                        noise_q = gr.Slider(
+                            0.0,
+                            4.0,
+                            value=1.0,
+                            step=0.01,
+                            label="Fall-off exponent (lower=higher detail)",
+                        )
+                        color_variation = gr.Slider(
+                            0.0,
+                            1.0,
+                            value=0.05,
+                            step=0.01,
+                            label="Color variation",
+                        )
+                    with gr.Row():
+                        height = gr.Slider(
+                            384, 786, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 786, value=args.width, step=8, label="Width"
+                        )
+                        precision = gr.Radio(
+                            label="Precision",
+                            value=args.precision,
+                            choices=[
+                                "fp16",
+                                "fp32",
+                            ],
+                            visible=False,
+                        )
+                        max_length = gr.Radio(
+                            label="Max Length",
+                            value=args.max_length,
+                            choices=[
+                                64,
+                                77,
+                            ],
+                            visible=False,
+                        )
+                    with gr.Row():
+                        steps = gr.Slider(
+                            1, 100, value=20, step=1, label="Steps"
+                        )
+                    with gr.Row():
+                        guidance_scale = gr.Slider(
+                            0,
+                            50,
+                            value=args.guidance_scale,
+                            step=0.1,
+                            label="CFG Scale",
+                        )
+                        batch_count = gr.Slider(
+                            1,
+                            100,
+                            value=args.batch_count,
+                            step=1,
+                            label="Batch Count",
+                            interactive=True,
+                        )
+                        batch_size = gr.Slider(
+                            1,
+                            4,
+                            value=args.batch_size,
+                            step=1,
+                            label="Batch Size",
+                            interactive=False,
+                            visible=False,
+                        )
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    random_seed = gr.Button("Randomize Seed")
+                    random_seed.click(
+                        None,
+                        inputs=[],
+                        outputs=[seed],
+                        _js="() => Math.floor(Math.random() * 4294967295)",
+                    )
+                    stable_diffusion = gr.Button("Generate Image(s)")
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    gallery = gr.Gallery(
+                        label="Generated images",
+                        show_label=False,
+                        elem_id="gallery",
+                    ).style(grid=[2])
+                    std_output = gr.Textbox(
+                        value="Nothing to show.",
+                        lines=1,
+                        show_label=False,
+                    )
+                output_dir = args.output_dir if args.output_dir else Path.cwd()
+                output_dir = Path(output_dir, "generated_imgs")
+                output_loc = gr.Textbox(
+                    label="Saving Images at",
+                    value=output_dir,
+                    interactive=False,
+                )
+        kwargs = dict(
+            fn=outpaint_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                init_image,
+                pixels,
+                mask_blur,
+                directions,
+                noise_q,
+                color_variation,
+                height,
+                width,
+                steps,
+                guidance_scale,
+                seed,
+                batch_count,
+                batch_size,
+                scheduler,
+                custom_model,
+                hf_model_id,
+                precision,
+                device,
+                max_length,
+                save_metadata_to_json,
+                save_metadata_to_png,
+            ],
+            outputs=[gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt.submit(**kwargs)
+        negative_prompt.submit(**kwargs)
+        stable_diffusion.click(**kwargs)

apps/stable_diffusion/web/ui/txt2img_ui.py

@@ -0,0 +1,229 @@
+import os
+import sys
+import glob
+from pathlib import Path
+import gradio as gr
+from PIL import Image
+from apps.stable_diffusion.scripts import txt2img_inf
+from apps.stable_diffusion.src import prompt_examples, args
+from apps.stable_diffusion.web.ui.utils import (
+    available_devices,
+    nodlogo_loc,
+)
+
+
+with gr.Blocks(title="Text-to-Image") as txt2img_web:
+    with gr.Row(elem_id="ui_title"):
+        nod_logo = Image.open(nodlogo_loc)
+        with gr.Row():
+            with gr.Column(scale=1, elem_id="demo_title_outer"):
+                gr.Image(
+                    value=nod_logo,
+                    show_label=False,
+                    interactive=False,
+                    elem_id="top_logo",
+                ).style(width=150, height=50)
+    with gr.Row(elem_id="ui_body"):
+        with gr.Row():
+            with gr.Column(scale=1, min_width=600):
+                with gr.Row():
+                    ckpt_path = (
+                        Path(args.ckpt_dir)
+                        if args.ckpt_dir
+                        else Path(Path.cwd(), "models")
+                    )
+                    ckpt_path.mkdir(parents=True, exist_ok=True)
+                    types = (
+                        "*.ckpt",
+                        "*.safetensors",
+                    )  # the tuple of file types
+                    ckpt_files = ["None"]
+                    for extn in types:
+                        files = glob.glob(os.path.join(ckpt_path, extn))
+                        ckpt_files.extend(files)
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {ckpt_path})",
+                        value=args.ckpt_loc if args.ckpt_loc else "None",
+                        choices=ckpt_files
+                        + [
+                            "Linaqruf/anything-v3.0",
+                            "prompthero/openjourney",
+                            "wavymulder/Analog-Diffusion",
+                            "stabilityai/stable-diffusion-2-1",
+                            "stabilityai/stable-diffusion-2-1-base",
+                            "CompVis/stable-diffusion-v1-4",
+                        ],
+                    )
+                    hf_model_id = gr.Textbox(
+                        placeholder="Select 'None' in the Models dropdown on the left and enter model ID here e.g: SG161222/Realistic_Vision_V1.3",
+                        value="",
+                        label="HuggingFace Model ID",
+                        lines=3,
+                    )
+
+                with gr.Group(elem_id="prompt_box_outer"):
+                    prompt = gr.Textbox(
+                        label="Prompt",
+                        value=args.prompts[0],
+                        lines=1,
+                        elem_id="prompt_box",
+                    )
+                    negative_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        value=args.negative_prompts[0],
+                        lines=1,
+                        elem_id="negative_prompt_box",
+                    )
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            label="Scheduler",
+                            value=args.scheduler,
+                            choices=[
+                                "DDIM",
+                                "PNDM",
+                                "LMSDiscrete",
+                                "KDPM2Discrete",
+                                "DPMSolverMultistep",
+                                "EulerDiscrete",
+                                "EulerAncestralDiscrete",
+                                "SharkEulerDiscrete",
+                            ],
+                        )
+                        with gr.Group():
+                            save_metadata_to_png = gr.Checkbox(
+                                label="Save prompt information to PNG",
+                                value=args.write_metadata_to_png,
+                                interactive=True,
+                            )
+                            save_metadata_to_json = gr.Checkbox(
+                                label="Save prompt information to JSON file",
+                                value=args.save_metadata_to_json,
+                                interactive=True,
+                            )
+                    with gr.Row():
+                        height = gr.Slider(
+                            384, 786, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 786, value=args.width, step=8, label="Width"
+                        )
+                        precision = gr.Radio(
+                            label="Precision",
+                            value=args.precision,
+                            choices=[
+                                "fp16",
+                                "fp32",
+                            ],
+                            visible=False,
+                        )
+                        max_length = gr.Radio(
+                            label="Max Length",
+                            value=args.max_length,
+                            choices=[
+                                64,
+                                77,
+                            ],
+                            visible=False,
+                        )
+                    with gr.Row():
+                        steps = gr.Slider(
+                            1, 100, value=args.steps, step=1, label="Steps"
+                        )
+                        guidance_scale = gr.Slider(
+                            0,
+                            50,
+                            value=args.guidance_scale,
+                            step=0.1,
+                            label="CFG Scale",
+                        )
+                    with gr.Row():
+                        batch_count = gr.Slider(
+                            1,
+                            100,
+                            value=args.batch_count,
+                            step=1,
+                            label="Batch Count",
+                            interactive=True,
+                        )
+                        batch_size = gr.Slider(
+                            1,
+                            4,
+                            value=args.batch_size,
+                            step=1,
+                            label="Batch Size",
+                            interactive=True,
+                        )
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    random_seed = gr.Button("Randomize Seed")
+                    random_seed.click(
+                        None,
+                        inputs=[],
+                        outputs=[seed],
+                        _js="() => Math.floor(Math.random() * 4294967295)",
+                    )
+                    stable_diffusion = gr.Button("Generate Image(s)")
+                with gr.Accordion(label="Prompt Examples!", open=False):
+                    ex = gr.Examples(
+                        examples=prompt_examples,
+                        inputs=prompt,
+                        cache_examples=False,
+                        elem_id="prompt_examples",
+                    )
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    gallery = gr.Gallery(
+                        label="Generated images",
+                        show_label=False,
+                        elem_id="gallery",
+                    ).style(grid=[2])
+                    std_output = gr.Textbox(
+                        value="Nothing to show.",
+                        lines=1,
+                        show_label=False,
+                    )
+                output_dir = args.output_dir if args.output_dir else Path.cwd()
+                output_dir = Path(output_dir, "generated_imgs")
+                output_loc = gr.Textbox(
+                    label="Saving Images at",
+                    value=output_dir,
+                    interactive=False,
+                )
+        kwargs = dict(
+            fn=txt2img_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                height,
+                width,
+                steps,
+                guidance_scale,
+                seed,
+                batch_count,
+                batch_size,
+                scheduler,
+                custom_model,
+                hf_model_id,
+                precision,
+                device,
+                max_length,
+                save_metadata_to_json,
+                save_metadata_to_png,
+            ],
+            outputs=[gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt.submit(**kwargs)
+        negative_prompt.submit(**kwargs)
+        stable_diffusion.click(**kwargs)

apps/stable_diffusion/web/ui/utils.py

@@ -0,0 +1,15 @@
+import os
+import sys
+from apps.stable_diffusion.src import get_available_devices
+
+
+def resource_path(relative_path):
+    """Get absolute path to resource, works for dev and for PyInstaller"""
+    base_path = getattr(
+        sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__))
+    )
+    return os.path.join(base_path, relative_path)
+
+
+nodlogo_loc = resource_path("logos/nod-logo.png")
+available_devices = get_available_devices()

apps/stable_diffusion/web/utils/gradio_configs.py

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

build_tools/image_comparison.py

@@ -0,0 +1,51 @@
+import argparse
+from PIL import Image
+import numpy as np
+
+import requests
+import shutil
+import os
+import subprocess
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-n", "--newfile")
+parser.add_argument(
+    "-g",
+    "--golden_url",
+    default="https://storage.googleapis.com/shark_tank/testdata/cyberpunk_fores_42_0_230119_021148.png",
+)
+
+
+def get_image(url, local_filename):
+    res = requests.get(url, stream=True)
+    if res.status_code == 200:
+        with open(local_filename, "wb") as f:
+            shutil.copyfileobj(res.raw, f)
+
+
+def compare_images(new_filename, golden_filename):
+    new = np.array(Image.open(new_filename)) / 255.0
+    golden = np.array(Image.open(golden_filename)) / 255.0
+    diff = np.abs(new - golden)
+    mean = np.mean(diff)
+    if mean > 0.1:
+        if os.name != "nt":
+            subprocess.run(
+                [
+                    "gsutil",
+                    "cp",
+                    new_filename,
+                    "gs://shark_tank/testdata/builder/",
+                ]
+            )
+        raise SystemExit("new and golden not close")
+    else:
+        print("SUCCESS")
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    tempfile_name = os.path.join(os.getcwd(), "golden.png")
+    get_image(args.golden_url, tempfile_name)
+    compare_images(args.newfile, tempfile_name)

build_tools/populate_sharktank_ci.sh

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

build_tools/stable_diffusion_testing.py

@@ -0,0 +1,143 @@
+import os
+from sys import executable
+import subprocess
+from apps.stable_diffusion.src.utils.resources import (
+    get_json_file,
+)
+from datetime import datetime as dt
+from shark.shark_downloader import download_public_file
+from image_comparison import compare_images
+import argparse
+from glob import glob
+import shutil
+import requests
+
+model_config_dicts = get_json_file(
+    os.path.join(
+        os.getcwd(),
+        "apps/stable_diffusion/src/utils/resources/model_config.json",
+    )
+)
+
+
+def get_inpaint_inputs():
+    os.mkdir("./test_images/inputs")
+    img_url = (
+        "https://huggingface.co/datasets/diffusers/test-arrays/resolve"
+        "/main/stable_diffusion_inpaint/input_bench_image.png"
+    )
+    mask_url = (
+        "https://huggingface.co/datasets/diffusers/test-arrays/resolve"
+        "/main/stable_diffusion_inpaint/input_bench_mask.png"
+    )
+    img = requests.get(img_url)
+    mask = requests.get(mask_url)
+    open("./test_images/inputs/image.png", "wb").write(img.content)
+    open("./test_images/inputs/mask.png", "wb").write(mask.content)
+
+
+def test_loop(device="vulkan", beta=False, extra_flags=[]):
+    # Get golden values from tank
+    shutil.rmtree("./test_images", ignore_errors=True)
+    os.mkdir("./test_images")
+    os.mkdir("./test_images/golden")
+    get_inpaint_inputs()
+    hf_model_names = model_config_dicts[0].values()
+    tuned_options = ["--no-use_tuned", "--use_tuned"]
+    import_options = ["--import_mlir", "--no-import_mlir"]
+    prompt_text = "--prompt=cyberpunk forest by Salvador Dali"
+    inpaint_prompt_text = "--prompt=Face of a yellow cat, high resolution, sitting on a park bench"
+    if os.name == "nt":
+        prompt_text = '--prompt="cyberpunk forest by Salvador Dali"'
+        inpaint_prompt_text = '--prompt="Face of a yellow cat, high resolution, sitting on a park bench"'
+    if beta:
+        extra_flags.append("--beta_models=True")
+    for import_opt in import_options:
+        for model_name in hf_model_names:
+            if model_name == "Linaqruf/anything-v3.0":
+                continue
+            for use_tune in tuned_options:
+                command = (
+                    [
+                        executable,  # executable is the python from the venv used to run this
+                        "apps/stable_diffusion/scripts/txt2img.py",
+                        "--device=" + device,
+                        prompt_text,
+                        "--negative_prompts=" + '""',
+                        "--seed=42",
+                        import_opt,
+                        "--output_dir="
+                        + os.path.join(os.getcwd(), "test_images", model_name),
+                        "--hf_model_id=" + model_name,
+                        use_tune,
+                    ]
+                    if "inpainting" not in model_name
+                    else [
+                        "python",
+                        "apps/stable_diffusion/scripts/inpaint.py",
+                        "--device=" + device,
+                        inpaint_prompt_text,
+                        "--negative_prompts=" + '""',
+                        "--img_path=./test_images/inputs/image.png",
+                        "--mask_path=./test_images/inputs/mask.png",
+                        "--seed=42",
+                        "--import_mlir",
+                        "--output_dir="
+                        + os.path.join(os.getcwd(), "test_images", model_name),
+                        "--hf_model_id=" + model_name,
+                        use_tune,
+                    ]
+                )
+                command += extra_flags
+                if os.name == "nt":
+                    command = " ".join(command)
+                generated_image = not subprocess.call(
+                    command, stdout=subprocess.DEVNULL
+                )
+                if os.name != "nt":
+                    command = " ".join(command)
+                if generated_image:
+                    print(command)
+                    print("Successfully generated image")
+                    os.makedirs(
+                        "./test_images/golden/" + model_name, exist_ok=True
+                    )
+                    download_public_file(
+                        "gs://shark_tank/testdata/golden/" + model_name,
+                        "./test_images/golden/" + model_name,
+                    )
+                    test_file_path = os.path.join(
+                        os.getcwd(),
+                        "test_images",
+                        model_name,
+                        "generated_imgs",
+                        dt.now().strftime("%Y%m%d"),
+                        "*.png",
+                    )
+                    test_file = glob(test_file_path)[0]
+
+                    golden_path = (
+                        "./test_images/golden/" + model_name + "/*.png"
+                    )
+                    golden_file = glob(golden_path)[0]
+                    compare_images(test_file, golden_file)
+                else:
+                    print(command)
+                    print("failed to generate image for this configuration")
+                    if "2_1_base" in model_name:
+                        print("failed a known successful model.")
+                        exit(1)
+
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-d", "--device", default="vulkan")
+parser.add_argument(
+    "-b", "--beta", action=argparse.BooleanOptionalAction, default=False
+)
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    print(args)
+    test_loop(args.device, args.beta, [])

conftest.py

@@ -60,3 +60,13 @@ def pytest_addoption(parser):
         default="gs://shark_tank/latest",
         help="URL to bucket from which to download SHARK tank artifacts. Default is gs://shark_tank/latest",
     )
+    parser.addoption(
+        "--benchmark_dispatches",
+        default=None,
+        help="Benchmark individual dispatch kernels produced by IREE compiler. Use 'All' for all, or specific dispatches e.g. '0 1 2 10'",
+    )
+    parser.addoption(
+        "--dispatch_benchmarks_dir",
+        default="./temp_dispatch_benchmarks",
+        help="Directory in which dispatch benchmarks are saved.",
+    )

dataset/README.md

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

dataset/annotation_tool.py

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

dataset/args.py

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

dataset/requirements.txt

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

dataset/utils.py

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

generate_sharktank.py

@@ -2,33 +2,26 @@
 """SHARK Tank"""
 # python generate_sharktank.py, you have to give a csv tile with [model_name, model_download_url]
 # will generate local shark tank folder like this:
-#   HOME
-#     /.local
-#       /shark_tank
-#           /albert_lite_base
-#           /...model_name...
+#   /SHARK
+#     /gen_shark_tank
+#       /albert_lite_base
+#       /...model_name...
 #
 
 import os
 import csv
 import argparse
 from shark.shark_importer import SharkImporter
-from shark.parser import shark_args
-import tensorflow as tf
 import subprocess as sp
 import hashlib
 import numpy as np
 from pathlib import Path
-
-visible_default = tf.config.list_physical_devices("GPU")
-try:
-    tf.config.set_visible_devices([], "GPU")
-    visible_devices = tf.config.get_visible_devices()
-    for device in visible_devices:
-        assert device.device_type != "GPU"
-except:
-    # Invalid device or cannot modify virtual devices once initialized.
-    pass
+from apps.stable_diffusion.src.models import (
+    model_wrappers as mw,
+)
+from apps.stable_diffusion.src.utils.stable_args import (
+    args,
+)
 
 
 def create_hash(file_name):
@@ -41,9 +34,12 @@ def create_hash(file_name):
 
 
 def save_torch_model(torch_model_list):
-    from tank.model_utils import get_hf_model
-    from tank.model_utils import get_vision_model
-    from tank.model_utils import get_hf_img_cls_model
+    from tank.model_utils import (
+        get_hf_model,
+        get_vision_model,
+        get_hf_img_cls_model,
+        get_fp16_model,
+    )
 
     with open(torch_model_list) as csvfile:
         torch_reader = csv.reader(csvfile, delimiter=",")
@@ -59,13 +55,39 @@ def save_torch_model(torch_model_list):
 
             model = None
             input = None
+            if model_type == "stable_diffusion":
+                args.use_tuned = False
+                args.import_mlir = True
+                args.use_tuned = False
+                args.local_tank_cache = WORKDIR
+
+                precision_values = ["fp16"]
+                seq_lengths = [64, 77]
+                for precision_value in precision_values:
+                    args.precision = precision_value
+                    for length in seq_lengths:
+                        model = mw.SharkifyStableDiffusionModel(
+                            model_id=torch_model_name,
+                            custom_weights="",
+                            precision=precision_value,
+                            max_len=length,
+                            width=512,
+                            height=512,
+                            use_base_vae=False,
+                            debug=True,
+                            sharktank_dir=WORKDIR,
+                            generate_vmfb=False,
+                        )
+                        model()
+                continue
             if model_type == "vision":
                 model, input, _ = get_vision_model(torch_model_name)
             elif model_type == "hf":
                 model, input, _ = get_hf_model(torch_model_name)
             elif model_type == "hf_img_cls":
                 model, input, _ = get_hf_img_cls_model(torch_model_name)
-
+            elif model_type == "fp16":
+                model, input, _ = get_fp16_model(torch_model_name)
             torch_model_name = torch_model_name.replace("/", "_")
             torch_model_dir = os.path.join(
                 WORKDIR, str(torch_model_name) + "_torch"
@@ -106,6 +128,17 @@ def save_tf_model(tf_model_list):
         get_keras_model,
         get_TFhf_model,
     )
+    import tensorflow as tf
+
+    visible_default = tf.config.list_physical_devices("GPU")
+    try:
+        tf.config.set_visible_devices([], "GPU")
+        visible_devices = tf.config.get_visible_devices()
+        for device in visible_devices:
+            assert device.device_type != "GPU"
+    except:
+        # Invalid device or cannot modify virtual devices once initialized.
+        pass
 
     with open(tf_model_list) as csvfile:
         tf_reader = csv.reader(csvfile, delimiter=",")
@@ -129,13 +162,13 @@ def save_tf_model(tf_model_list):
             tf_model_name = tf_model_name.replace("/", "_")
             tf_model_dir = os.path.join(WORKDIR, str(tf_model_name) + "_tf")
             os.makedirs(tf_model_dir, exist_ok=True)
-
             mlir_importer = SharkImporter(
                 model,
-                input,
+                inputs=input,
                 frontend="tf",
             )
             mlir_importer.import_debug(
+                is_dynamic=False,
                 dir=tf_model_dir,
                 model_name=tf_model_name,
             )
@@ -201,51 +234,48 @@ def is_valid_file(arg):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--torch_model_csv",
-        type=lambda x: is_valid_file(x),
-        default="./tank/torch_model_list.csv",
-        help="""Contains the file with torch_model name and args.
-             Please see: https://github.com/nod-ai/SHARK/blob/main/tank/torch_model_list.csv""",
-    )
-    parser.add_argument(
-        "--tf_model_csv",
-        type=lambda x: is_valid_file(x),
-        default="./tank/tf_model_list.csv",
-        help="Contains the file with tf model name and args.",
-    )
-    parser.add_argument(
-        "--tflite_model_csv",
-        type=lambda x: is_valid_file(x),
-        default="./tank/tflite/tflite_model_list.csv",
-        help="Contains the file with tf model name and args.",
-    )
-    parser.add_argument(
-        "--ci_tank_dir",
-        type=bool,
-        default=False,
-    )
-    parser.add_argument("--upload", type=bool, default=False)
+    # Note, all of these flags are overridden by the import of args from stable_args.py, flags are duplicated temporarily to preserve functionality
+    # parser = argparse.ArgumentParser()
+    # parser.add_argument(
+    #    "--torch_model_csv",
+    #    type=lambda x: is_valid_file(x),
+    #    default="./tank/torch_model_list.csv",
+    #    help="""Contains the file with torch_model name and args.
+    #         Please see: https://github.com/nod-ai/SHARK/blob/main/tank/torch_model_list.csv""",
+    # )
+    # parser.add_argument(
+    #    "--tf_model_csv",
+    #    type=lambda x: is_valid_file(x),
+    #    default="./tank/tf_model_list.csv",
+    #    help="Contains the file with tf model name and args.",
+    # )
+    # parser.add_argument(
+    #    "--tflite_model_csv",
+    #    type=lambda x: is_valid_file(x),
+    #    default="./tank/tflite/tflite_model_list.csv",
+    #    help="Contains the file with tf model name and args.",
+    # )
+    # parser.add_argument(
+    #    "--ci_tank_dir",
+    #    type=bool,
+    #    default=False,
+    # )
+    # parser.add_argument("--upload", type=bool, default=False)
 
-    args = parser.parse_args()
+    # old_args = parser.parse_args()
 
     home = str(Path.home())
-    if args.ci_tank_dir == True:
-        WORKDIR = os.path.join(os.path.dirname(__file__), "gen_shark_tank")
-    else:
-        WORKDIR = os.path.join(home, ".local/shark_tank/")
+    WORKDIR = os.path.join(os.path.dirname(__file__), "gen_shark_tank")
+    torch_model_csv = os.path.join(
+        os.path.dirname(__file__), "tank", "torch_model_list.csv"
+    )
+    tf_model_csv = os.path.join(
+        os.path.dirname(__file__), "tank", "tf_model_list.csv"
+    )
+    tflite_model_csv = os.path.join(
+        os.path.dirname(__file__), "tank", "tflite", "tflite_model_list.csv"
+    )
 
-    if args.torch_model_csv:
-        save_torch_model(args.torch_model_csv)
-
-    if args.tf_model_csv:
-        save_tf_model(args.tf_model_csv)
-
-    if args.tflite_model_csv:
-        save_tflite_model(args.tflite_model_csv)
-
-    if args.upload:
-        git_hash = sp.getoutput("git log -1 --format='%h'") + "/"
-        print("uploading files to gs://shark_tank/" + git_hash)
-        os.system(f"gsutil cp -r {WORKDIR}* gs://shark_tank/" + git_hash)
+    save_torch_model(torch_model_csv)
+    save_tf_model(tf_model_csv)
+    save_tflite_model(tflite_model_csv)

process_skipfiles.py

@@ -0,0 +1,44 @@
+# This script will toggle the comment/uncommenting aspect for dealing
+# with __file__ AttributeError arising in case of a few modules in
+# `torch/_dynamo/skipfiles.py` (within shark.venv)
+
+from distutils.sysconfig import get_python_lib
+import fileinput
+from pathlib import Path
+
+# Diffusers 0.13.1 fails with transformers __init.py errros in BLIP. So remove it for now until we fork it
+pix2pix_file = Path(
+    get_python_lib()
+    + "/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py"
+)
+if pix2pix_file.exists():
+    print("Removing..%s", pix2pix_file)
+    pix2pix_file.unlink()
+
+
+path_to_skipfiles = Path(get_python_lib() + "/torch/_dynamo/skipfiles.py")
+
+modules_to_comment = ["abc,", "os,", "posixpath,", "_collections_abc,"]
+startMonitoring = 0
+for line in fileinput.input(path_to_skipfiles, inplace=True):
+    if "SKIP_DIRS = " in line:
+        startMonitoring = 1
+        print(line, end="")
+    elif startMonitoring in [1, 2]:
+        if "]" in line:
+            startMonitoring += 1
+            print(line, end="")
+        else:
+            flag = True
+            for module in modules_to_comment:
+                if module in line:
+                    if not line.startswith("#"):
+                        print(f"#{line}", end="")
+                    else:
+                        print(f"{line[1:]}", end="")
+                    flag = False
+                    break
+            if flag:
+                print(line, end="")
+    else:
+        print(line, end="")

requirements-importer.txt

@@ -1,8 +1,10 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
 --pre
 
-numpy==1.22.4
+numpy>1.22.4
 torchvision
+pytorch-triton
+tabulate
 
 tqdm
 
@@ -13,8 +15,8 @@ iree-tools-tf
 
 # TensorFlow and JAX.
 gin-config
-tensorflow==2.10
-keras==2.10
+tensorflow>=2.10.1
+keras>=2.10
 #tf-models-nightly
 #tensorflow-text-nightly
 transformers

requirements.txt

@@ -10,16 +10,20 @@ google-cloud-storage
 # Testing
 pytest
 pytest-xdist
+pytest-forked
 Pillow
 parameterized
 
 # Add transformers, diffusers and scipy since it most commonly used
 transformers
-diffusers
+diffusers @ git+https://github.com/nod-ai/diffusers@2226767529c805d7997d1a9f218437f2c7fb65e1
 scipy
 ftfy
 gradio
 altair
+omegaconf
+safetensors
 
 # Keep PyInstaller at the end. Sometimes Windows Defender flags it but most folks can continue even if it errors
+pefile
 pyinstaller

setup.py

@@ -2,11 +2,12 @@ from setuptools import find_packages
 from setuptools import setup
 
 import os
+import glob
 
 with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
-PACKAGE_VERSION = os.environ.get("SHARK_PACKAGE_VERSION") or "0.0.4"
+PACKAGE_VERSION = os.environ.get("SHARK_PACKAGE_VERSION") or "0.0.5"
 backend_deps = []
 if "NO_BACKEND" in os.environ.keys():
     backend_deps = [
@@ -34,6 +35,7 @@ setup(
     ],
     packages=find_packages(exclude=("examples")),
     python_requires=">=3.9",
+    data_files=glob.glob("apps/stable_diffusion/resources/**"),
     install_requires=[
         "numpy",
         "PyYAML",

setup_venv.ps1

@@ -1,13 +1,54 @@
-#Write-Host "Installing python"
+<#
+.SYNOPSIS
+  A script to update and install the SHARK runtime and its dependencies.
 
-#Start-Process winget install Python.Python.3.10 '/quiet InstallAllUsers=1 PrependPath=1' -wait -NoNewWindow
+.DESCRIPTION
+  This script updates and installs the SHARK runtime and its dependencies.
+  It checks the Python version installed and installs any required build
+  dependencies into a Python virtual environment.
+  If that environment does not exist, it creates it.
+  
+.PARAMETER update-src
+  git pulls latest version
 
-#Write-Host "python installation completed successfully"
+.PARAMETER force
+  removes and recreates venv to force update of all dependencies
+  
+.EXAMPLE
+  .\setup_venv.ps1 --force
 
-#Write-Host "Reload environment variables"
-#$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
-#Write-Host "Reloaded environment variables"
+.EXAMPLE
+  .\setup_venv.ps1 --update-src
 
+.INPUTS
+  None
+
+.OUTPUTS
+  None
+
+#>
+
+param([string]$arguments)
+
+if ($arguments -eq "--update-src"){
+	git pull
+}
+
+if ($arguments -eq "--force"){
+	if (Test-Path env:VIRTUAL_ENV) {
+        Write-Host "deactivating..."
+        Deactivate
+    }
+    
+    if (Test-Path .\shark.venv\) {
+        Write-Host "removing and recreating venv..."
+        Remove-Item .\shark.venv -Force -Recurse
+        if (Test-Path .\shark.venv\) {
+            Write-Host 'could not remove .\shark-venv - please try running ".\setup_venv.ps1 --force" again!'
+            break
+        }
+    }
+}
 
 # redirect stderr into stdout
 $p = &{python -V} 2>&1
@@ -19,19 +60,36 @@ $version = if($p -is [System.Management.Automation.ErrorRecord])
 }
 else
 {
-    # otherwise return as is
-    $p
+    # otherwise return complete Python list
+    $ErrorActionPreference = 'SilentlyContinue'
+    $PyVer = py --list
 }
 
-Write-Host "Python version found is"
-Write-Host $p
+# deactivate any activated venvs
+if ($PyVer -like "*venv*")
+{
+  deactivate # make sure we don't update the wrong venv
+  $PyVer = py --list # update list
+}
 
+Write-Host "Python versions found are"
+Write-Host ($PyVer | Out-String) # formatted output with line breaks
+if (!($PyVer.length -ne 0)) {$p} # return Python --version String if py.exe is unavailable
+if (!($PyVer -like "*3.11*") -and !($p -like "*3.11*")) # if 3.11 is not in any list
+{
+    Write-Host "Please install Python 3.11 and try again"
+    break
+}
 
 Write-Host "Installing Build Dependencies"
-python -m venv .\shark.venv\
+# make sure we really use 3.11 from list, even if it's not the default.
+if (!($PyVer.length -ne 0)) {py -3.11 -m venv .\shark.venv\}
+else {python -m venv .\shark.venv\}
 .\shark.venv\Scripts\activate
+python -m pip install --upgrade pip
+pip install wheel
 pip install -r requirements.txt
-pip install --pre torch-mlir torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/
+pip install --pre torch-mlir torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/
 pip install --upgrade -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html iree-compiler iree-runtime
 Write-Host "Building SHARK..."
 pip install -e . -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html

setup_venv.sh

@@ -42,7 +42,7 @@ Green=`tput setaf 2`
 Yellow=`tput setaf 3`
 
 # Assume no binary torch-mlir.
-# Currently available for macOS m1&intel (3.10) and Linux(3.7,3.8,3.9,3.10)
+# Currently available for macOS m1&intel (3.11) and Linux(3.8,3.10,3.11)
 torch_mlir_bin=false
 if [[ $(uname -s) = 'Darwin' ]]; then
   echo "${Yellow}Apple macOS detected"
@@ -60,12 +60,12 @@ if [[ $(uname -s) = 'Darwin' ]]; then
   fi
   echo "${Yellow}Run the following commands to setup your SSL certs for your Python version if you see SSL errors with tests"
   echo "${Yellow}/Applications/Python\ 3.XX/Install\ Certificates.command"
-  if [ "$PYTHON_VERSION_X_Y" == "3.10" ]; then
+  if [ "$PYTHON_VERSION_X_Y" == "3.11" ]; then
     torch_mlir_bin=true
   fi
 elif [[ $(uname -s) = 'Linux' ]]; then
   echo "${Yellow}Linux detected"
-  if [ "$PYTHON_VERSION_X_Y" == "3.7" ] || [ "$PYTHON_VERSION_X_Y" == "3.8" ]  || [ "$PYTHON_VERSION_X_Y" == "3.9" ] || [ "$PYTHON_VERSION_X_Y" == "3.10" ] ; then
+  if [ "$PYTHON_VERSION_X_Y" == "3.8" ]  || [ "$PYTHON_VERSION_X_Y" == "3.10" ] || [ "$PYTHON_VERSION_X_Y" == "3.11" ] ; then
     torch_mlir_bin=true
   fi
 else
@@ -89,7 +89,7 @@ if [ "$torch_mlir_bin" = true ]; then
   fi
 else
   echo "${Red}No binaries found for Python $PYTHON_VERSION_X_Y on $(uname -s)"
-  echo "${Yello}Python 3.10 supported on macOS and 3.7,3.8,3.9 and 3.10 on Linux"
+  echo "${Yello}Python 3.11 supported on macOS and 3.8,3.10 and 3.11 on Linux"
   echo "${Red}Please build torch-mlir from source in your environment"
   exit 1
 fi
@@ -98,7 +98,7 @@ if [[ -z "${USE_IREE}" ]]; then
   RUNTIME="https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html"
 else
   touch ./.use-iree
-  RUNTIME="https://iree-org.github.io/iree/pip-release-links.html"
+  RUNTIME="https://openxla.github.io/iree/pip-release-links.html"
 fi
 if [[ -z "${NO_BACKEND}" ]]; then
   echo "Installing ${RUNTIME}..."
@@ -112,7 +112,7 @@ if [[ ! -z "${IMPORTER}" ]]; then
   if [[ $(uname -s) = 'Linux' ]]; then
     echo "${Yellow}Linux detected.. installing Linux importer tools"
     #Always get the importer tools from upstream IREE
-    $PYTHON -m pip install --no-warn-conflicts --upgrade -r "$TD/requirements-importer.txt" -f https://iree-org.github.io/iree/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+    $PYTHON -m pip install --no-warn-conflicts --upgrade -r "$TD/requirements-importer.txt" -f https://openxla.github.io/iree/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
   elif [[ $(uname -s) = 'Darwin' ]]; then
     echo "${Yellow}macOS detected.. installing macOS importer tools"
     #Conda seems to have some problems installing these packages and hope they get resolved upstream.
@@ -123,8 +123,13 @@ fi
 $PYTHON -m pip install --no-warn-conflicts -e . -f https://llvm.github.io/torch-mlir/package-index/ -f ${RUNTIME} -f https://download.pytorch.org/whl/nightly/torch/
 
 if [[ $(uname -s) = 'Linux' && ! -z "${BENCHMARK}" ]]; then
+  T_VER=$($PYTHON -m pip show torch | grep Version)
+  TORCH_VERSION=${T_VER:9:17}
+  TV_VER=$($PYTHON -m pip show torchvision | grep Version)
+  TV_VERSION=${TV_VER:9:18}
   $PYTHON -m pip uninstall -y torch torchvision
-  $PYTHON -m pip install --pre torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cu117
+  $PYTHON -m pip install -U --pre --no-warn-conflicts triton
+  $PYTHON -m pip install --no-deps https://download.pytorch.org/whl/nightly/cu117/torch-${TORCH_VERSION}%2Bcu117-cp311-cp311-linux_x86_64.whl https://download.pytorch.org/whl/nightly/cu117/torchvision-${TV_VERSION}%2Bcu117-cp311-cp311-linux_x86_64.whl
   if [ $? -eq 0 ];then
     echo "Successfully Installed torch + cu117."
   else

shark/examples/shark_dynamo/basic_examples.py

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

shark/examples/shark_inference/ESRGAN/esrgan.py

@@ -128,7 +128,6 @@ def load_mlir(mlir_loc):
 
 
 def compile_through_fx(model, inputs, mlir_loc=None):
-
     module = load_mlir(mlir_loc)
     if module == None:
         fx_g = make_fx(

shark/examples/shark_inference/sharded_bloom.py

@@ -0,0 +1,698 @@
+####################################################################################
+# Please make sure you have transformers 4.21.2 installed before running this demo
+#
+# -p --model_path: the directory in which you want to store the bloom files.
+# -dl --device_list: the list of device indices you want to use.  if you want to only use the first device, or you are running on cpu leave this blank.
+#                     Otherwise, please give this argument in this format: "[0, 1, 2]"
+# -de --device: the device you want to run bloom on.  E.G. cpu, cuda
+# -c, --recompile: set to true if you want to recompile to vmfb.
+# -d, --download: set to true if you want to redownload the mlir files
+# -t --token_count: the number of tokens you want to generate
+# -pr --prompt: the prompt you want to feed to the model
+# -m --model_namme: the name of the model, e.g. bloom-560m
+#####################################################################################
+
+import os
+import io
+import torch
+import torch.nn as nn
+from collections import OrderedDict
+import torch_mlir
+from torch_mlir import TensorPlaceholder
+import re
+from transformers.models.bloom.configuration_bloom import BloomConfig
+import json
+import sys
+import argparse
+import json
+import urllib.request
+
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch._decomp import get_decompositions
+from shark.shark_inference import SharkInference
+from shark.shark_downloader import download_public_file
+from transformers import (
+    BloomTokenizerFast,
+    BloomForSequenceClassification,
+    BloomForCausalLM,
+)
+from transformers.models.bloom.modeling_bloom import (
+    BloomBlock,
+    build_alibi_tensor,
+)
+
+IS_CUDA = False
+
+
+class ShardedBloom:
+    def __init__(self, src_folder):
+        f = open(f"{src_folder}/config.json")
+        config = json.load(f)
+        f.close()
+
+        self.layers_initialized = False
+
+        self.src_folder = src_folder
+        try:
+            self.n_embed = config["n_embed"]
+        except KeyError:
+            self.n_embed = config["hidden_size"]
+        self.vocab_size = config["vocab_size"]
+        self.n_layer = config["n_layer"]
+        try:
+            self.n_head = config["num_attention_heads"]
+        except KeyError:
+            self.n_head = config["n_head"]
+
+    def _init_layer(self, layer_name, device, replace, device_idx):
+        if replace or not os.path.exists(
+            f"{self.src_folder}/{layer_name}.vmfb"
+        ):
+            f_ = open(f"{self.src_folder}/{layer_name}.mlir", encoding="utf-8")
+            module = f_.read()
+            f_.close()
+            module = bytes(module, "utf-8")
+            shark_module = SharkInference(
+                module,
+                device=device,
+                mlir_dialect="tm_tensor",
+                device_idx=device_idx,
+            )
+            shark_module.save_module(
+                module_name=f"{self.src_folder}/{layer_name}",
+                extra_args=[
+                    "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
+                    "--iree-stream-resource-max-allocation-size=1000000000",
+                    "--iree-codegen-check-ir-before-llvm-conversion=false",
+                ],
+            )
+        else:
+            shark_module = SharkInference(
+                "",
+                device=device,
+                mlir_dialect="tm_tensor",
+                device_idx=device_idx,
+            )
+
+        return shark_module
+
+    def init_layers(self, device, replace=False, device_idx=[0]):
+        if device_idx is not None:
+            n_devices = len(device_idx)
+
+        self.word_embeddings_module = self._init_layer(
+            "word_embeddings",
+            device,
+            replace,
+            device_idx if device_idx is None else device_idx[0 % n_devices],
+        )
+        self.word_embeddings_layernorm_module = self._init_layer(
+            "word_embeddings_layernorm",
+            device,
+            replace,
+            device_idx if device_idx is None else device_idx[1 % n_devices],
+        )
+        self.ln_f_module = self._init_layer(
+            "ln_f",
+            device,
+            replace,
+            device_idx if device_idx is None else device_idx[2 % n_devices],
+        )
+        self.lm_head_module = self._init_layer(
+            "lm_head",
+            device,
+            replace,
+            device_idx if device_idx is None else device_idx[3 % n_devices],
+        )
+        self.block_modules = [
+            self._init_layer(
+                f"bloom_block_{i}",
+                device,
+                replace,
+                device_idx
+                if device_idx is None
+                else device_idx[(i + 4) % n_devices],
+            )
+            for i in range(self.n_layer)
+        ]
+
+        self.layers_initialized = True
+
+    def load_layers(self):
+        assert self.layers_initialized
+
+        self.word_embeddings_module.load_module(
+            f"{self.src_folder}/word_embeddings.vmfb"
+        )
+        self.word_embeddings_layernorm_module.load_module(
+            f"{self.src_folder}/word_embeddings_layernorm.vmfb"
+        )
+        for block_module, i in zip(self.block_modules, range(self.n_layer)):
+            block_module.load_module(f"{self.src_folder}/bloom_block_{i}.vmfb")
+        self.ln_f_module.load_module(f"{self.src_folder}/ln_f.vmfb")
+        self.lm_head_module.load_module(f"{self.src_folder}/lm_head.vmfb")
+
+    def forward_pass(self, input_ids, device):
+        if IS_CUDA:
+            cudaSetDevice(self.word_embeddings_module.device_idx)
+
+        input_embeds = self.word_embeddings_module(
+            inputs=(input_ids,), function_name="forward"
+        )
+
+        input_embeds = torch.tensor(input_embeds).float()
+        if IS_CUDA:
+            cudaSetDevice(self.word_embeddings_layernorm_module.device_idx)
+        hidden_states = self.word_embeddings_layernorm_module(
+            inputs=(input_embeds,), function_name="forward"
+        )
+
+        hidden_states = torch.tensor(hidden_states).float()
+
+        attention_mask = torch.ones(
+            [hidden_states.shape[0], len(input_ids[0])]
+        )
+        alibi = build_alibi_tensor(
+            attention_mask,
+            self.n_head,
+            hidden_states.dtype,
+            hidden_states.device,
+        )
+
+        causal_mask = _prepare_attn_mask(
+            attention_mask, input_ids.size(), input_embeds, 0
+        )
+        causal_mask = torch.tensor(causal_mask).float()
+
+        presents = ()
+        all_hidden_states = tuple(hidden_states)
+
+        for block_module, i in zip(self.block_modules, range(self.n_layer)):
+            if IS_CUDA:
+                cudaSetDevice(block_module.device_idx)
+
+            output = block_module(
+                inputs=(
+                    hidden_states.detach().numpy(),
+                    alibi.detach().numpy(),
+                    causal_mask.detach().numpy(),
+                ),
+                function_name="forward",
+            )
+            hidden_states = torch.tensor(output[0]).float()
+            all_hidden_states = all_hidden_states + (hidden_states,)
+            presents = presents + (
+                tuple(
+                    (
+                        output[1],
+                        output[2],
+                    )
+                ),
+            )
+        if IS_CUDA:
+            cudaSetDevice(self.ln_f_module.device_idx)
+
+        hidden_states = self.ln_f_module(
+            inputs=(hidden_states,), function_name="forward"
+        )
+        if IS_CUDA:
+            cudaSetDevice(self.lm_head_module.device_idx)
+
+        logits = self.lm_head_module(
+            inputs=(hidden_states,), function_name="forward"
+        )
+        logits = torch.tensor(logits).float()
+
+        return torch.argmax(logits[:, -1, :], dim=-1)
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size,
+    dtype: torch.dtype,
+    past_key_values_length: int = 0,
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    batch_size, target_length = input_ids_shape
+    mask = torch.full((target_length, target_length), torch.finfo(dtype).min)
+    mask_cond = torch.arange(mask.size(-1))
+    intermediate_mask = mask_cond < (mask_cond + 1).view(mask.size(-1), 1)
+    mask.masked_fill_(intermediate_mask, 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat(
+            [
+                torch.zeros(
+                    target_length, past_key_values_length, dtype=dtype
+                ),
+                mask,
+            ],
+            dim=-1,
+        )
+    expanded_mask = mask[None, None, :, :].expand(
+        batch_size, 1, target_length, target_length + past_key_values_length
+    )
+    return expanded_mask
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: int = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    batch_size, source_length = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else source_length
+
+    expanded_mask = (
+        mask[:, None, None, :]
+        .expand(batch_size, 1, tgt_len, source_length)
+        .to(dtype)
+    )
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+def _prepare_attn_mask(
+    attention_mask, input_shape, inputs_embeds, past_key_values_length
+):
+    # create causal mask
+    # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+    combined_attention_mask = None
+    if input_shape[-1] > 1:
+        combined_attention_mask = _make_causal_mask(
+            input_shape,
+            inputs_embeds.dtype,
+            past_key_values_length=past_key_values_length,
+        ).to(attention_mask.device)
+
+    if attention_mask is not None:
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        expanded_attn_mask = _expand_mask(
+            attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+        )
+        combined_attention_mask = (
+            expanded_attn_mask
+            if combined_attention_mask is None
+            else expanded_attn_mask + combined_attention_mask
+        )
+
+    return combined_attention_mask
+
+
+def download_model(destination_folder, model_name):
+    download_public_file(
+        f"https://{model_name}/config.json", destination_folder
+    )
+    f = open(f"{destination_folder}/config.json")
+    config = json.load(f)
+    f.close()
+    n_blocks = config["n_layer"]
+    download_public_file(
+        f"https://{model_name}/lm_head.mlir", destination_folder
+    )
+    download_public_file(f"https://{model_name}/ln_f.mlir", destination_folder)
+    download_public_file(
+        f"https://{model_name}/word_embeddings.mlir", destination_folder
+    )
+    download_public_file(
+        f"https://{model_name}/word_embeddings_layernorm.mlir",
+        destination_folder,
+    )
+    download_public_file(
+        f"https://{model_name}/tokenizer.json", destination_folder
+    )
+
+    for i in range(n_blocks):
+        download_public_file(
+            f"https://{model_name}/bloom_block_{i}.mlir", destination_folder
+        )
+
+
+def compile_embeddings(embeddings_layer, input_ids, path):
+    input_ids_placeholder = torch_mlir.TensorPlaceholder.like(
+        input_ids, dynamic_axes=[1]
+    )
+    module = torch_mlir.compile(
+        embeddings_layer,
+        (input_ids_placeholder),
+        torch_mlir.OutputType.LINALG_ON_TENSORS,
+        use_tracing=False,
+        verbose=False,
+    )
+
+    bytecode_stream = io.BytesIO()
+    module.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+
+    f_ = open(path, "w+")
+    f_.write(str(module))
+    f_.close()
+    return
+
+
+def compile_word_embeddings_layernorm(
+    embeddings_layer_layernorm, embeds, path
+):
+    embeds_placeholder = torch_mlir.TensorPlaceholder.like(
+        embeds, dynamic_axes=[1]
+    )
+    module = torch_mlir.compile(
+        embeddings_layer_layernorm,
+        (embeds_placeholder),
+        torch_mlir.OutputType.LINALG_ON_TENSORS,
+        use_tracing=False,
+        verbose=False,
+    )
+
+    bytecode_stream = io.BytesIO()
+    module.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+
+    f_ = open(path, "w+")
+    f_.write(str(module))
+    f_.close()
+    return
+
+
+def strip_overloads(gm):
+    """
+    Modifies the target of graph nodes in :attr:`gm` to strip overloads.
+    Args:
+        gm(fx.GraphModule): The input Fx graph module to be modified
+    """
+    for node in gm.graph.nodes:
+        if isinstance(node.target, torch._ops.OpOverload):
+            node.target = node.target.overloadpacket
+    gm.recompile()
+
+
+def compile_to_mlir(
+    bblock,
+    hidden_states,
+    layer_past=None,
+    attention_mask=None,
+    head_mask=None,
+    use_cache=None,
+    output_attentions=False,
+    alibi=None,
+    block_index=0,
+    path=".",
+):
+    fx_g = make_fx(
+        bblock,
+        decomposition_table=get_decompositions(
+            [
+                torch.ops.aten.split.Tensor,
+                torch.ops.aten.split_with_sizes,
+            ]
+        ),
+        tracing_mode="real",
+        _allow_non_fake_inputs=False,
+    )(hidden_states, alibi, attention_mask)
+
+    fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
+    fx_g.recompile()
+
+    strip_overloads(fx_g)
+
+    hidden_states_placeholder = TensorPlaceholder.like(
+        hidden_states, dynamic_axes=[1]
+    )
+    attention_mask_placeholder = TensorPlaceholder.like(
+        attention_mask, dynamic_axes=[2, 3]
+    )
+    alibi_placeholder = TensorPlaceholder.like(alibi, dynamic_axes=[2])
+
+    ts_g = torch.jit.script(fx_g)
+
+    module = torch_mlir.compile(
+        ts_g,
+        (
+            hidden_states_placeholder,
+            alibi_placeholder,
+            attention_mask_placeholder,
+        ),
+        torch_mlir.OutputType.LINALG_ON_TENSORS,
+        use_tracing=False,
+        verbose=False,
+    )
+
+    module_placeholder = module
+    module_context = module_placeholder.context
+
+    def check_valid_line(line, line_n, mlir_file_len):
+        if "private" in line:
+            return False
+        if "attributes" in line:
+            return False
+        if mlir_file_len - line_n == 2:
+            return False
+
+        return True
+
+    mlir_file_len = len(str(module).split("\n"))
+
+    def remove_constant_dim(line):
+        if "17x" in line:
+            line = re.sub("17x", "?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 eq" in line:
+            line = re.sub("c17", "dim", line)
+        if " 17," in line:
+            line = re.sub(" 17,", " %dim,", line)
+        return line
+
+    module = "\n".join(
+        [
+            remove_constant_dim(line)
+            for line, line_n in zip(
+                str(module).split("\n"), range(mlir_file_len)
+            )
+            if check_valid_line(line, line_n, mlir_file_len)
+        ]
+    )
+
+    module = module_placeholder.parse(module, context=module_context)
+    bytecode_stream = io.BytesIO()
+    module.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+
+    f_ = open(path, "w+")
+    f_.write(str(module))
+    f_.close()
+    return
+
+
+def compile_ln_f(ln_f, hidden_layers, path):
+    hidden_layers_placeholder = torch_mlir.TensorPlaceholder.like(
+        hidden_layers, dynamic_axes=[1]
+    )
+    module = torch_mlir.compile(
+        ln_f,
+        (hidden_layers_placeholder),
+        torch_mlir.OutputType.LINALG_ON_TENSORS,
+        use_tracing=False,
+        verbose=False,
+    )
+
+    bytecode_stream = io.BytesIO()
+    module.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+
+    f_ = open(path, "w+")
+    f_.write(str(module))
+    f_.close()
+    return
+
+
+def compile_lm_head(lm_head, hidden_layers, path):
+    hidden_layers_placeholder = torch_mlir.TensorPlaceholder.like(
+        hidden_layers, dynamic_axes=[1]
+    )
+    module = torch_mlir.compile(
+        lm_head,
+        (hidden_layers_placeholder),
+        torch_mlir.OutputType.LINALG_ON_TENSORS,
+        use_tracing=False,
+        verbose=False,
+    )
+
+    bytecode_stream = io.BytesIO()
+    module.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+
+    f_ = open(path, "w+")
+    f_.write(str(module))
+    f_.close()
+    return
+
+
+def create_mlirs(destination_folder, model_name):
+    model_config = "bigscience/" + model_name
+    sample_input_ids = torch.ones([1, 17], dtype=torch.int64)
+
+    urllib.request.urlretrieve(
+        f"https://huggingface.co/bigscience/{model_name}/resolve/main/config.json",
+        filename=f"{destination_folder}/config.json",
+    )
+    urllib.request.urlretrieve(
+        f"https://huggingface.co/bigscience/bloom/resolve/main/tokenizer.json",
+        filename=f"{destination_folder}/tokenizer.json",
+    )
+
+    class HuggingFaceLanguage(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.model = BloomForCausalLM.from_pretrained(model_config)
+
+        def forward(self, tokens):
+            return self.model.forward(tokens)[0]
+
+    class HuggingFaceBlock(torch.nn.Module):
+        def __init__(self, block):
+            super().__init__()
+            self.model = block
+
+        def forward(self, tokens, alibi, attention_mask):
+            output = self.model(
+                hidden_states=tokens,
+                alibi=alibi,
+                attention_mask=attention_mask,
+                use_cache=True,
+                output_attentions=False,
+            )
+            return (output[0], output[1][0], output[1][1])
+
+    model = HuggingFaceLanguage()
+
+    compile_embeddings(
+        model.model.transformer.word_embeddings,
+        sample_input_ids,
+        f"{destination_folder}/word_embeddings.mlir",
+    )
+
+    inputs_embeds = model.model.transformer.word_embeddings(sample_input_ids)
+
+    compile_word_embeddings_layernorm(
+        model.model.transformer.word_embeddings_layernorm,
+        inputs_embeds,
+        f"{destination_folder}/word_embeddings_layernorm.mlir",
+    )
+
+    hidden_states = model.model.transformer.word_embeddings_layernorm(
+        inputs_embeds
+    )
+
+    input_shape = sample_input_ids.size()
+
+    current_sequence_length = hidden_states.shape[1]
+    past_key_values_length = 0
+    past_key_values = tuple([None] * len(model.model.transformer.h))
+
+    attention_mask = torch.ones(
+        (hidden_states.shape[0], current_sequence_length), device="cpu"
+    )
+
+    alibi = build_alibi_tensor(
+        attention_mask,
+        model.model.transformer.n_head,
+        hidden_states.dtype,
+        "cpu",
+    )
+
+    causal_mask = _prepare_attn_mask(
+        attention_mask, input_shape, inputs_embeds, past_key_values_length
+    )
+
+    head_mask = model.model.transformer.get_head_mask(
+        None, model.model.transformer.config.n_layer
+    )
+    output_attentions = model.model.transformer.config.output_attentions
+
+    all_hidden_states = ()
+
+    for i, (block, layer_past) in enumerate(
+        zip(model.model.transformer.h, past_key_values)
+    ):
+        all_hidden_states = all_hidden_states + (hidden_states,)
+
+        proxy_model = HuggingFaceBlock(block)
+
+        compile_to_mlir(
+            proxy_model,
+            hidden_states,
+            layer_past=layer_past,
+            attention_mask=causal_mask,
+            head_mask=head_mask[i],
+            use_cache=True,
+            output_attentions=output_attentions,
+            alibi=alibi,
+            block_index=i,
+            path=f"{destination_folder}/bloom_block_{i}.mlir",
+        )
+
+    compile_ln_f(
+        model.model.transformer.ln_f,
+        hidden_states,
+        f"{destination_folder}/ln_f.mlir",
+    )
+    hidden_states = model.model.transformer.ln_f(hidden_states)
+    compile_lm_head(
+        model.model.lm_head,
+        hidden_states,
+        f"{destination_folder}/lm_head.mlir",
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(prog="Bloom-560m")
+    parser.add_argument("-p", "--model_path")
+    parser.add_argument("-dl", "--device_list", default=None)
+    parser.add_argument("-de", "--device", default="cpu")
+    parser.add_argument("-c", "--recompile", default=False, type=bool)
+    parser.add_argument("-d", "--download", default=False, type=bool)
+    parser.add_argument("-t", "--token_count", default=10, type=int)
+    parser.add_argument("-m", "--model_name", default="bloom-560m")
+    parser.add_argument(
+        "-pr",
+        "--prompt",
+        default="The SQL command to extract all the users whose name starts with A is: ",
+    )
+    args = parser.parse_args()
+
+    if args.device_list is not None:
+        args.device_list = json.loads(args.device_list)
+
+    if args.device == "cuda" and args.device_list is not None:
+        IS_CUDA = True
+        from cuda.cudart import cudaSetDevice
+    if args.download:
+        # download_model(args.model_path, args.model_name)
+        create_mlirs(args.model_path, args.model_name)
+    from transformers import AutoTokenizer, AutoModelForCausalLM, BloomConfig
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model_path)
+    input_ids = tokenizer.encode(args.prompt, return_tensors="pt")
+
+    shardedbloom = ShardedBloom(args.model_path)
+    shardedbloom.init_layers(
+        device=args.device, replace=args.recompile, device_idx=args.device_list
+    )
+    shardedbloom.load_layers()
+
+    for _ in range(args.token_count):
+        next_token = shardedbloom.forward_pass(
+            torch.tensor(input_ids), device=args.device
+        )
+        input_ids = torch.cat([input_ids, next_token.unsqueeze(-1)], dim=-1)
+
+    print(tokenizer.decode(input_ids.squeeze()))

shark/examples/shark_inference/simple_dlrm.py

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

shark/examples/shark_inference/sparse_arch.py

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

shark/examples/shark_inference/stable_diff.py

@@ -1,272 +0,0 @@
-from transformers import CLIPTextModel, CLIPTokenizer
-from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
-import torch
-from PIL import Image
-from diffusers import LMSDiscreteScheduler
-from tqdm.auto import tqdm
-from shark.shark_inference import SharkInference
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch._decomp import get_decompositions
-import torch_mlir
-import tempfile
-import numpy as np
-
-# pip install diffusers
-# pip install scipy
-
-############### Parsing args #####################
-import argparse
-
-p = argparse.ArgumentParser(
-    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
-)
-
-p.add_argument(
-    "--prompt",
-    type=str,
-    default="a photograph of an astronaut riding a horse",
-    help="the text prompt to use",
-)
-p.add_argument("--device", type=str, default="cpu", help="the device to use")
-p.add_argument("--steps", type=int, default=10, help="the device to use")
-p.add_argument("--mlir_loc", type=str, default=None, help="the device to use")
-p.add_argument("--vae_loc", type=str, default=None, help="the device to use")
-args = p.parse_args()
-
-#####################################################
-
-
-def load_mlir(mlir_loc):
-    import os
-
-    if mlir_loc == None:
-        return None
-    print(f"Trying to load the model from {mlir_loc}.")
-    with open(os.path.join(mlir_loc)) as f:
-        mlir_module = f.read()
-    return mlir_module
-
-
-def compile_through_fx(model, inputs, mlir_loc=None, extra_args=[]):
-
-    module = load_mlir(mlir_loc)
-    if mlir_loc == None:
-        fx_g = make_fx(
-            model,
-            decomposition_table=get_decompositions(
-                [
-                    torch.ops.aten.embedding_dense_backward,
-                    torch.ops.aten.native_layer_norm_backward,
-                    torch.ops.aten.slice_backward,
-                    torch.ops.aten.select_backward,
-                    torch.ops.aten.norm.ScalarOpt_dim,
-                    torch.ops.aten.native_group_norm,
-                    torch.ops.aten.upsample_bilinear2d.vec,
-                    torch.ops.aten.split.Tensor,
-                    torch.ops.aten.split_with_sizes,
-                ]
-            ),
-        )(*inputs)
-
-        fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
-        fx_g.recompile()
-
-        def strip_overloads(gm):
-            """
-            Modifies the target of graph nodes in :attr:`gm` to strip overloads.
-            Args:
-                gm(fx.GraphModule): The input Fx graph module to be modified
-            """
-            for node in gm.graph.nodes:
-                if isinstance(node.target, torch._ops.OpOverload):
-                    node.target = node.target.overloadpacket
-            gm.recompile()
-
-        strip_overloads(fx_g)
-
-        ts_g = torch.jit.script(fx_g)
-
-        module = torch_mlir.compile(
-            ts_g,
-            inputs,
-            torch_mlir.OutputType.LINALG_ON_TENSORS,
-            use_tracing=False,
-            verbose=False,
-        )
-
-    mlir_model = module
-    func_name = "forward"
-
-    shark_module = SharkInference(
-        mlir_model,
-        func_name,
-        device=args.device,
-        mlir_dialect="tm_tensor",
-    )
-    shark_module.compile(extra_args)
-
-    return shark_module
-
-
-if __name__ == "__main__":
-
-    YOUR_TOKEN = "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"
-
-    # 1. Load the autoencoder model which will be used to decode the latents into image space.
-    vae = AutoencoderKL.from_pretrained(
-        "CompVis/stable-diffusion-v1-4",
-        subfolder="vae",
-        use_auth_token=YOUR_TOKEN,
-    )
-
-    # 2. Load the tokenizer and text encoder to tokenize and encode the text.
-    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-    text_encoder = CLIPTextModel.from_pretrained(
-        "openai/clip-vit-large-patch14"
-    )
-
-    class VaeModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.vae = AutoencoderKL.from_pretrained(
-                "CompVis/stable-diffusion-v1-4",
-                subfolder="vae",
-                use_auth_token=YOUR_TOKEN,
-            )
-
-        def forward(self, input):
-            return self.vae.decode(input, return_dict=False)[0]
-
-    vae = VaeModel()
-    vae_input = torch.rand(1, 4, 64, 64)
-    shark_vae = compile_through_fx(vae, (vae_input,), args.vae_loc)
-
-    # Wrap the unet model to return tuples.
-    class UnetModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.unet = UNet2DConditionModel.from_pretrained(
-                "CompVis/stable-diffusion-v1-4",
-                subfolder="unet",
-                use_auth_token=YOUR_TOKEN,
-            )
-            self.in_channels = self.unet.in_channels
-            self.train(False)
-
-        def forward(self, x, y, z):
-            return self.unet.forward(x, y, z, return_dict=False)[0]
-
-    # 3. The UNet model for generating the latents.
-    unet = UnetModel()
-    latent_model_input = torch.rand([2, 4, 64, 64])
-    text_embeddings = torch.rand([2, 77, 768])
-    shark_unet = compile_through_fx(
-        unet,
-        (latent_model_input, torch.tensor([1.0]), text_embeddings),
-        args.mlir_loc,
-        ["--iree-flow-enable-conv-nchw-to-nhwc-transform"],
-    )
-
-    # torch.jit.script(unet)
-
-    scheduler = LMSDiscreteScheduler(
-        beta_start=0.00085,
-        beta_end=0.012,
-        beta_schedule="scaled_linear",
-        num_train_timesteps=1000,
-    )
-
-    prompt = [args.prompt]
-
-    height = 512  # default height of Stable Diffusion
-    width = 512  # default width of Stable Diffusion
-
-    num_inference_steps = args.steps  # Number of denoising steps
-
-    guidance_scale = 7.5  # Scale for classifier-free guidance
-
-    generator = torch.manual_seed(
-        42
-    )  # Seed generator to create the inital latent noise
-
-    batch_size = len(prompt)
-
-    text_input = tokenizer(
-        prompt,
-        padding="max_length",
-        max_length=tokenizer.model_max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-
-    text_embeddings = text_encoder(text_input.input_ids)[0]
-
-    max_length = text_input.input_ids.shape[-1]
-    uncond_input = tokenizer(
-        [""] * batch_size,
-        padding="max_length",
-        max_length=max_length,
-        return_tensors="pt",
-    )
-    uncond_embeddings = text_encoder(uncond_input.input_ids)[0]
-
-    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-    latents = torch.randn(
-        (batch_size, unet.in_channels, height // 8, width // 8),
-        generator=generator,
-    )
-    # latents = latents.to(torch_device)
-
-    scheduler.set_timesteps(num_inference_steps)
-
-    latents = latents * scheduler.sigmas[0]
-    # print(latents, latents.shape)
-
-    for i, t in tqdm(enumerate(scheduler.timesteps)):
-
-        print(f"i = {i} t = {t}")
-        # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
-        latent_model_input = torch.cat([latents] * 2)
-        sigma = scheduler.sigmas[i]
-        latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
-
-        # predict the noise residual
-
-        # with torch.no_grad():
-        # noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings)
-
-        latent_model_input_numpy = latent_model_input.detach().numpy()
-        text_embeddings_numpy = text_embeddings.detach().numpy()
-
-        noise_pred = shark_unet.forward(
-            (
-                latent_model_input_numpy,
-                np.array([t]).astype(np.float32),
-                text_embeddings_numpy,
-            )
-        )
-        noise_pred = torch.from_numpy(noise_pred)
-
-        # perform guidance
-        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-        noise_pred = noise_pred_uncond + guidance_scale * (
-            noise_pred_text - noise_pred_uncond
-        )
-
-        # compute the previous noisy sample x_t -> x_t-1
-        latents = scheduler.step(noise_pred, i, latents)["prev_sample"]
-
-    # print("Latents shape : ", latents.shape)
-
-    # scale and decode the image latents with vae
-    latents = 1 / 0.18215 * latents
-    latents_numpy = latents.detach().numpy()
-    image = shark_vae.forward((latents_numpy,))
-    image = torch.from_numpy(image)
-
-    image = (image / 2 + 0.5).clamp(0, 1)
-    image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
-    images = (image * 255).round().astype("uint8")
-    pil_images = [Image.fromarray(image) for image in images]
-    pil_images[0].save("astro.jpg")

shark/examples/shark_inference/stable_diff_f16.py

@@ -1,280 +0,0 @@
-from transformers import CLIPTextModel, CLIPTokenizer
-from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
-import torch
-from PIL import Image
-from diffusers import LMSDiscreteScheduler
-from tqdm.auto import tqdm
-from shark.shark_inference import SharkInference
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch._decomp import get_decompositions
-import torch_mlir
-import tempfile
-import numpy as np
-
-# pip install diffusers
-# pip install scipy
-
-############### Parsing args #####################
-import argparse
-
-p = argparse.ArgumentParser(
-    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
-)
-
-p.add_argument(
-    "--prompt",
-    type=str,
-    default="a photograph of an astronaut riding a horse",
-    help="the text prompt to use",
-)
-p.add_argument("--device", type=str, default="cpu", help="the device to use")
-p.add_argument("--steps", type=int, default=50, help="the device to use")
-p.add_argument("--mlir_loc", type=str, default=None, help="the device to use")
-p.add_argument("--vae_loc", type=str, default=None, help="the device to use")
-args = p.parse_args()
-
-#####################################################
-
-
-def fp16_unet():
-    from shark.shark_downloader import download_model
-
-    mlir_model, func_name, inputs, golden_out = download_model(
-        "stable_diff_f16_18_OCT",
-        tank_url="gs://shark_tank/prashant_nod",
-        frontend="torch",
-    )
-    shark_module = SharkInference(
-        mlir_model, func_name, device=args.device, mlir_dialect="linalg"
-    )
-    shark_module.compile()
-    return shark_module
-
-
-def load_mlir(mlir_loc):
-    import os
-
-    if mlir_loc == None:
-        return None
-    print(f"Trying to load the model from {mlir_loc}.")
-    with open(os.path.join(mlir_loc)) as f:
-        mlir_module = f.read()
-    return mlir_module
-
-
-def compile_through_fx(model, inputs, mlir_loc=None):
-
-    module = load_mlir(mlir_loc)
-    if mlir_loc == None:
-        fx_g = make_fx(
-            model,
-            decomposition_table=get_decompositions(
-                [
-                    torch.ops.aten.embedding_dense_backward,
-                    torch.ops.aten.native_layer_norm_backward,
-                    torch.ops.aten.slice_backward,
-                    torch.ops.aten.select_backward,
-                    torch.ops.aten.norm.ScalarOpt_dim,
-                    torch.ops.aten.native_group_norm,
-                    torch.ops.aten.upsample_bilinear2d.vec,
-                    torch.ops.aten.split.Tensor,
-                    torch.ops.aten.split_with_sizes,
-                ]
-            ),
-        )(*inputs)
-
-        fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
-        fx_g.recompile()
-
-        def strip_overloads(gm):
-            """
-            Modifies the target of graph nodes in :attr:`gm` to strip overloads.
-            Args:
-                gm(fx.GraphModule): The input Fx graph module to be modified
-            """
-            for node in gm.graph.nodes:
-                if isinstance(node.target, torch._ops.OpOverload):
-                    node.target = node.target.overloadpacket
-            gm.recompile()
-
-        strip_overloads(fx_g)
-
-        ts_g = torch.jit.script(fx_g)
-
-        module = torch_mlir.compile(
-            ts_g,
-            inputs,
-            torch_mlir.OutputType.LINALG_ON_TENSORS,
-            use_tracing=False,
-            verbose=False,
-        )
-
-    mlir_model = module
-    func_name = "forward"
-
-    shark_module = SharkInference(
-        mlir_model, func_name, device=args.device, mlir_dialect="linalg"
-    )
-    shark_module.compile()
-
-    return shark_module
-
-
-if __name__ == "__main__":
-
-    YOUR_TOKEN = "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"
-
-    # 1. Load the autoencoder model which will be used to decode the latents into image space.
-    vae = AutoencoderKL.from_pretrained(
-        "CompVis/stable-diffusion-v1-4",
-        subfolder="vae",
-        use_auth_token=YOUR_TOKEN,
-    )
-
-    # 2. Load the tokenizer and text encoder to tokenize and encode the text.
-    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-    text_encoder = CLIPTextModel.from_pretrained(
-        "openai/clip-vit-large-patch14"
-    )
-
-    class VaeModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.vae = AutoencoderKL.from_pretrained(
-                "CompVis/stable-diffusion-v1-4",
-                subfolder="vae",
-                use_auth_token=YOUR_TOKEN,
-            )
-
-        def forward(self, input):
-            return self.vae.decode(input, return_dict=False)[0]
-
-    vae = VaeModel()
-    vae_input = torch.rand(1, 4, 64, 64)
-    shark_vae = compile_through_fx(vae, (vae_input,), args.vae_loc)
-
-    # Wrap the unet model to return tuples.
-    class UnetModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.unet = UNet2DConditionModel.from_pretrained(
-                "CompVis/stable-diffusion-v1-4",
-                subfolder="unet",
-                use_auth_token=YOUR_TOKEN,
-            )
-            self.in_channels = self.unet.in_channels
-            self.train(False)
-
-    def forward(self, x, y, z):
-        return self.unet.forward(x, y, z, return_dict=False)[0]
-
-    # # 3. The UNet model for generating the latents.
-    unet = UnetModel()
-
-    shark_unet = fp16_unet()
-
-    scheduler = LMSDiscreteScheduler(
-        beta_start=0.00085,
-        beta_end=0.012,
-        beta_schedule="scaled_linear",
-        num_train_timesteps=1000,
-    )
-
-    prompt = [args.prompt]
-
-    height = 512  # default height of Stable Diffusion
-    width = 512  # default width of Stable Diffusion
-
-    num_inference_steps = args.steps  # Number of denoising steps
-
-    guidance_scale = 7.5  # Scale for classifier-free guidance
-
-    generator = torch.manual_seed(
-        42
-    )  # Seed generator to create the inital latent noise
-
-    batch_size = len(prompt)
-
-    text_input = tokenizer(
-        prompt,
-        padding="max_length",
-        max_length=tokenizer.model_max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-
-    text_embeddings = text_encoder(text_input.input_ids)[0]
-
-    max_length = text_input.input_ids.shape[-1]
-    uncond_input = tokenizer(
-        [""] * batch_size,
-        padding="max_length",
-        max_length=max_length,
-        return_tensors="pt",
-    )
-    uncond_embeddings = text_encoder(uncond_input.input_ids)[0]
-
-    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-    latents = torch.randn(
-        (batch_size, unet.in_channels, height // 8, width // 8),
-        generator=generator,
-    )
-    # latents = latents.to(torch_device)
-
-    scheduler.set_timesteps(num_inference_steps)
-
-    latents = latents * scheduler.sigmas[0]
-    # print(latents, latents.shape)
-
-    for i, t in tqdm(enumerate(scheduler.timesteps)):
-
-        print(f"i = {i} t = {t}")
-        # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
-        latent_model_input = torch.cat([latents] * 2)
-        sigma = scheduler.sigmas[i]
-        latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
-
-        # predict the noise residual
-
-        # with torch.no_grad():
-        # noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings)
-
-        latent_model_input_numpy = (
-            latent_model_input.detach().numpy().astype(np.half)
-        )
-        text_embeddings_numpy = (
-            text_embeddings.detach().numpy().astype(np.half)
-        )
-
-        noise_pred = shark_unet.forward(
-            (
-                latent_model_input_numpy,
-                np.array([t]).astype(np.half),
-                text_embeddings_numpy,
-            )
-        )
-        noise_pred = torch.from_numpy(noise_pred).to(torch.float32)
-
-        # perform guidance
-        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-        noise_pred = noise_pred_uncond + guidance_scale * (
-            noise_pred_text - noise_pred_uncond
-        )
-
-        # compute the previous noisy sample x_t -> x_t-1
-        latents = scheduler.step(noise_pred, i, latents)["prev_sample"]
-
-    # print("Latents shape : ", latents.shape)
-
-    # scale and decode the image latents with vae
-    latents = 1 / 0.18215 * latents
-    latents_numpy = latents.detach().numpy()
-    image = shark_vae.forward((latents_numpy,))
-    image = torch.from_numpy(image)
-
-    image = (image / 2 + 0.5).clamp(0, 1)
-    image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
-    images = (image * 255).round().astype("uint8")
-    pil_images = [Image.fromarray(image) for image in images]
-    pil_images[0].save("astro.jpg")

shark/examples/shark_inference/stable_diff_tf.py

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

shark/examples/shark_inference/stable_diffusion/.gitignore

@@ -1,2 +0,0 @@
-*.vmfb
-*.jpg

shark/examples/shark_inference/stable_diffusion/README.md

@@ -1,56 +0,0 @@
-# STABLE DIFFUSION
-
-## Installation
-
-Follow setup instructions in the main [README.md](https://github.com/nod-ai/SHARK#readme) for regular usage. 
-
-## Debug commands and other advanced usage follows.
-
-```shell
-python main.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 main.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 shark/examples/shark_inference/stable_diffusion/main.py --precision=fp16 --device=vulkan --steps=50 --save_vmfb
-```
-
-## Capture an RGP trace
-
-```shell
-python shark/examples/shark_inference/stable_diffusion/main.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_file=/path/to/output/vmfb --entry_function=forward --device=vulkan --function_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_file=/path/to/output/vmfb --entry_function=forward --function_input=@arr_0.npy --function_input=1xf16 --function_input=@arr_2.npy --function_input=@arr_3.npy --function_input=@arr_4.npy  
-```
-
-## Using other supported Stable Diffusion variants with SHARK:
-
-Currently we support the following fine-tuned versions of Stable Diffusion:
-- [AnythingV3](https://huggingface.co/Linaqruf/anything-v3.0)
-- [Analog Diffusion](https://huggingface.co/wavymulder/Analog-Diffusion)
-
-use the flag `--variant=` to specify the model to be used.
-
-```shell
-python .\shark\examples\shark_inference\stable_diffusion\main.py --variant=anythingv3 --max_length=77 --prompt="1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden"
-```

shark/examples/shark_inference/stable_diffusion/download_hf_models.py

@@ -1,25 +0,0 @@
-from PIL import Image
-import requests
-
-from transformers import CLIPProcessor, CLIPModel
-
-model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
-
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
-
-inputs = processor(
-    text=["a photo of a cat", "a photo of a dog"],
-    images=image,
-    return_tensors="pt",
-    padding=True,
-)
-
-outputs = model(**inputs)
-logits_per_image = (
-    outputs.logits_per_image
-)  # this is the image-text similarity score
-probs = logits_per_image.softmax(
-    dim=1
-)  # we can take the softmax to get the label probabilities

shark/examples/shark_inference/stable_diffusion/main.py

@@ -1,253 +0,0 @@
-import os
-
-os.environ["AMD_ENABLE_LLPC"] = "1"
-
-from transformers import CLIPTextModel, CLIPTokenizer
-import torch
-from PIL import Image
-import torchvision.transforms as T
-from diffusers import (
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerDiscreteScheduler,
-)
-from tqdm.auto import tqdm
-import numpy as np
-from random import randint
-from stable_args import args
-
-# This has to come before importing cache objects
-if args.clear_all:
-    print("CLEARING ALL, EXPECT SEVERAL MINUTES TO RECOMPILE")
-    from glob import glob
-    import shutil
-
-    vmfbs = glob(os.path.join(os.getcwd(), "*.vmfb"))
-    for vmfb in vmfbs:
-        if os.path.exists(vmfb):
-            os.remove(vmfb)
-    home = os.path.expanduser("~")
-    if os.name == "nt":  # Windows
-        appdata = os.getenv("LOCALAPPDATA")
-        shutil.rmtree(os.path.join(appdata, "AMD/VkCache"), ignore_errors=True)
-        shutil.rmtree(os.path.join(home, "shark_tank"), ignore_errors=True)
-    elif os.name == "unix":
-        shutil.rmtree(os.path.join(home, ".cache/AMD/VkCache"))
-        shutil.rmtree(os.path.join(home, ".local/shark_tank"))
-
-
-from utils import set_init_device_flags
-
-from opt_params import get_unet, get_vae, get_clip
-from schedulers import (
-    SharkEulerDiscreteScheduler,
-)
-import time
-import sys
-from shark.iree_utils.compile_utils import dump_isas
-
-# 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()
-
-
-if __name__ == "__main__":
-
-    dtype = torch.float32 if args.precision == "fp32" else torch.half
-
-    prompt = args.prompts
-    neg_prompt = args.negative_prompts
-    height = 512  # default height of Stable Diffusion
-    width = 512  # default width of Stable Diffusion
-    if args.version == "v2_1":
-        height = 768
-        width = 768
-
-    num_inference_steps = args.steps  # Number of denoising steps
-
-    # Scale for classifier-free guidance
-    guidance_scale = torch.tensor(args.guidance_scale).to(torch.float32)
-
-    # Handle out of range seeds.
-    uint32_info = np.iinfo(np.uint32)
-    uint32_min, uint32_max = uint32_info.min, uint32_info.max
-    seed = args.seed
-    if seed < uint32_min or seed >= uint32_max:
-        seed = randint(uint32_min, uint32_max)
-    generator = torch.manual_seed(
-        seed
-    )  # Seed generator to create the inital latent noise
-
-    # TODO: Add support for batch_size > 1.
-    batch_size = len(prompt)
-    if batch_size != 1:
-        sys.exit("More than one prompt is not supported yet.")
-    if batch_size != len(neg_prompt):
-        sys.exit("prompts and negative prompts must be of same length")
-
-    set_init_device_flags()
-    clip = get_clip()
-    unet = get_unet()
-    vae = get_vae()
-    if args.dump_isa:
-        dump_isas(args.dispatch_benchmarks_dir)
-
-    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-    scheduler = DPMSolverMultistepScheduler.from_pretrained(
-        "CompVis/stable-diffusion-v1-4",
-        subfolder="scheduler",
-    )
-    cpu_scheduling = True
-    if args.version == "v2_1":
-        tokenizer = CLIPTokenizer.from_pretrained(
-            "stabilityai/stable-diffusion-2-1", subfolder="tokenizer"
-        )
-
-        scheduler = DPMSolverMultistepScheduler.from_pretrained(
-            "stabilityai/stable-diffusion-2-1",
-            subfolder="scheduler",
-        )
-
-    if args.version == "v2_1base" and args.variant == "stablediffusion":
-        tokenizer = CLIPTokenizer.from_pretrained(
-            "stabilityai/stable-diffusion-2-1-base", subfolder="tokenizer"
-        )
-
-        if args.use_compiled_scheduler:
-            scheduler = SharkEulerDiscreteScheduler.from_pretrained(
-                "stabilityai/stable-diffusion-2-1-base",
-                subfolder="scheduler",
-            )
-            scheduler.compile()
-            cpu_scheduling = False
-        else:
-            scheduler = EulerDiscreteScheduler.from_pretrained(
-                "stabilityai/stable-diffusion-2-1-base",
-                subfolder="scheduler",
-            )
-
-    # create a random initial latent.
-    latents = torch.randn(
-        (batch_size, 4, height // 8, width // 8),
-        generator=generator,
-        dtype=torch.float32,
-    ).to(dtype)
-    # Warmup phase to improve performance.
-    if args.warmup_count >= 1:
-        vae_warmup_input = torch.clone(latents).detach().numpy()
-        clip_warmup_input = torch.randint(1, 2, (2, args.max_length))
-    for i in range(args.warmup_count):
-        vae.forward((vae_warmup_input,))
-        clip.forward((clip_warmup_input,))
-
-    start = time.time()
-
-    text_input = tokenizer(
-        prompt,
-        padding="max_length",
-        max_length=args.max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-    max_length = text_input.input_ids.shape[-1]
-    uncond_input = tokenizer(
-        neg_prompt,
-        padding="max_length",
-        max_length=max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-    text_input = torch.cat([uncond_input.input_ids, text_input.input_ids])
-
-    clip_inf_start = time.time()
-    text_embeddings = clip.forward((text_input,))
-    clip_inf_end = time.time()
-    text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
-    text_embeddings_numpy = text_embeddings.detach().numpy()
-
-    scheduler.set_timesteps(num_inference_steps)
-    scheduler.is_scale_input_called = True
-
-    latents = latents * scheduler.init_noise_sigma
-
-    avg_ms = 0
-    for i, t in tqdm(enumerate(scheduler.timesteps), disable=args.hide_steps):
-        step_start = time.time()
-        if not args.hide_steps:
-            print(f"i = {i} t = {t}", end="")
-        timestep = torch.tensor([t]).to(dtype).detach().numpy()
-        latent_model_input = scheduler.scale_model_input(latents, t)
-        if cpu_scheduling:
-            latent_model_input = latent_model_input.detach().numpy()
-
-        profile_device = start_profiling(file_path="unet.rdc")
-
-        noise_pred = unet.forward(
-            (
-                latent_model_input,
-                timestep,
-                text_embeddings_numpy,
-                guidance_scale,
-            ),
-            send_to_host=False,
-        )
-
-        end_profiling(profile_device)
-
-        if cpu_scheduling:
-            noise_pred = torch.from_numpy(noise_pred.to_host())
-            latents = scheduler.step(noise_pred, t, latents).prev_sample
-        else:
-            latents = scheduler.step(noise_pred, t, latents)
-        step_time = time.time() - step_start
-        avg_ms += step_time
-        step_ms = int((step_time) * 1000)
-        if not args.hide_steps:
-            print(f" ({step_ms}ms)")
-
-    # scale and decode the image latents with vae
-    if args.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 = vae.forward((latents_numpy,))
-    vae_end = time.time()
-    end_profiling(profile_device)
-    if args.use_base_vae:
-        image = torch.from_numpy(images)
-        image = (image.detach().cpu() * 255.0).numpy()
-        images = image.round()
-    end_time = time.time()
-
-    avg_ms = 1000 * avg_ms / args.steps
-    clip_inf_time = (clip_inf_end - clip_inf_start) * 1000
-    vae_inf_time = (vae_end - vae_start) * 1000
-    total_time = end_time - start
-    print(f"\nAverage step time: {avg_ms}ms/it")
-    print(f"Clip Inference time (ms) = {clip_inf_time:.3f}")
-    print(f"VAE Inference time (ms): {vae_inf_time:.3f}")
-    print(f"\nTotal image generation time: {total_time}sec")
-
-    transform = T.ToPILImage()
-    pil_images = [
-        transform(image) for image in torch.from_numpy(images).to(torch.uint8)
-    ]
-    for i in range(batch_size):
-        pil_images[i].save(f"{args.prompts[i]}_{i}.jpg")

shark/examples/shark_inference/stable_diffusion/model_wrappers.py

@@ -1,285 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel
-from transformers import CLIPTextModel
-from utils import compile_through_fx
-from stable_args import args
-import torch
-
-model_config = {
-    "v2_1": "stabilityai/stable-diffusion-2-1",
-    "v2_1base": "stabilityai/stable-diffusion-2-1-base",
-    "v1_4": "CompVis/stable-diffusion-v1-4",
-}
-
-# clip has 2 variants of max length 77 or 64.
-model_clip_max_length = 64 if args.max_length == 64 else 77
-if args.variant in ["anythingv3", "analogdiffusion", "dreamlike"]:
-    model_clip_max_length = 77
-elif args.variant == "openjourney":
-    model_clip_max_length = 64
-
-model_variant = {
-    "stablediffusion": "SD",
-    "anythingv3": "Linaqruf/anything-v3.0",
-    "dreamlike": "dreamlike-art/dreamlike-diffusion-1.0",
-    "openjourney": "prompthero/openjourney",
-    "analogdiffusion": "wavymulder/Analog-Diffusion",
-}
-
-model_input = {
-    "v2_1": {
-        "clip": (torch.randint(1, 2, (2, model_clip_max_length)),),
-        "vae": (torch.randn(1, 4, 96, 96),),
-        "unet": (
-            torch.randn(1, 4, 96, 96),  # latents
-            torch.tensor([1]).to(torch.float32),  # timestep
-            torch.randn(2, model_clip_max_length, 1024),  # embedding
-            torch.tensor(1).to(torch.float32),  # guidance_scale
-        ),
-    },
-    "v2_1base": {
-        "clip": (torch.randint(1, 2, (2, model_clip_max_length)),),
-        "vae": (torch.randn(1, 4, 64, 64),),
-        "unet": (
-            torch.randn(1, 4, 64, 64),  # latents
-            torch.tensor([1]).to(torch.float32),  # timestep
-            torch.randn(2, model_clip_max_length, 1024),  # embedding
-            torch.tensor(1).to(torch.float32),  # guidance_scale
-        ),
-    },
-    "v1_4": {
-        "clip": (torch.randint(1, 2, (2, model_clip_max_length)),),
-        "vae": (torch.randn(1, 4, 64, 64),),
-        "unet": (
-            torch.randn(1, 4, 64, 64),
-            torch.tensor([1]).to(torch.float32),  # timestep
-            torch.randn(2, model_clip_max_length, 768),
-            torch.tensor(1).to(torch.float32),
-        ),
-    },
-}
-
-# revision param for from_pretrained defaults to "main" => fp32
-model_revision = {
-    "stablediffusion": "fp16" if args.precision == "fp16" else "main",
-    "anythingv3": "diffusers",
-    "analogdiffusion": "main",
-    "openjourney": "main",
-    "dreamlike": "main",
-}
-
-
-def get_clip_mlir(model_name="clip_text", extra_args=[]):
-
-    text_encoder = CLIPTextModel.from_pretrained(
-        "openai/clip-vit-large-patch14"
-    )
-    if args.variant == "stablediffusion":
-        if args.version != "v1_4":
-            text_encoder = CLIPTextModel.from_pretrained(
-                model_config[args.version], subfolder="text_encoder"
-            )
-
-    elif args.variant in [
-        "anythingv3",
-        "analogdiffusion",
-        "openjourney",
-        "dreamlike",
-    ]:
-        text_encoder = CLIPTextModel.from_pretrained(
-            model_variant[args.variant],
-            subfolder="text_encoder",
-            revision=model_revision[args.variant],
-        )
-    else:
-        raise ValueError(f"{args.variant} not yet added")
-
-    class CLIPText(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.text_encoder = text_encoder
-
-        def forward(self, input):
-            return self.text_encoder(input)[0]
-
-    clip_model = CLIPText()
-    shark_clip = compile_through_fx(
-        clip_model,
-        model_input[args.version]["clip"],
-        model_name=model_name,
-        extra_args=extra_args,
-    )
-    return shark_clip
-
-
-def get_base_vae_mlir(model_name="vae", extra_args=[]):
-    class BaseVaeModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.vae = AutoencoderKL.from_pretrained(
-                model_config[args.version]
-                if args.variant == "stablediffusion"
-                else model_variant[args.variant],
-                subfolder="vae",
-                revision=model_revision[args.variant],
-            )
-
-        def forward(self, input):
-            x = self.vae.decode(input, return_dict=False)[0]
-            return (x / 2 + 0.5).clamp(0, 1)
-
-    vae = BaseVaeModel()
-    if args.variant == "stablediffusion":
-        if args.precision == "fp16":
-            vae = vae.half().cuda()
-            inputs = tuple(
-                [
-                    inputs.half().cuda()
-                    for inputs in model_input[args.version]["vae"]
-                ]
-            )
-        else:
-            inputs = model_input[args.version]["vae"]
-    elif args.variant in [
-        "anythingv3",
-        "analogdiffusion",
-        "openjourney",
-        "dreamlike",
-    ]:
-        if args.precision == "fp16":
-            vae = vae.half().cuda()
-            inputs = tuple(
-                [inputs.half().cuda() for inputs in model_input["v1_4"]["vae"]]
-            )
-        else:
-            inputs = model_input["v1_4"]["vae"]
-    else:
-        raise ValueError(f"{args.variant} not yet added")
-
-    shark_vae = compile_through_fx(
-        vae,
-        inputs,
-        model_name=model_name,
-        extra_args=extra_args,
-    )
-    return shark_vae
-
-
-def get_vae_mlir(model_name="vae", extra_args=[]):
-    class VaeModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.vae = AutoencoderKL.from_pretrained(
-                model_config[args.version]
-                if args.variant == "stablediffusion"
-                else model_variant[args.variant],
-                subfolder="vae",
-                revision=model_revision[args.variant],
-            )
-
-        def forward(self, input):
-            input = 1 / 0.18215 * input
-            x = self.vae.decode(input, return_dict=False)[0]
-            x = (x / 2 + 0.5).clamp(0, 1)
-            x = x * 255.0
-            return x.round()
-
-    vae = VaeModel()
-    if args.variant == "stablediffusion":
-        if args.precision == "fp16":
-            vae = vae.half().cuda()
-            inputs = tuple(
-                [
-                    inputs.half().cuda()
-                    for inputs in model_input[args.version]["vae"]
-                ]
-            )
-        else:
-            inputs = model_input[args.version]["vae"]
-    elif args.variant in [
-        "anythingv3",
-        "analogdiffusion",
-        "openjourney",
-        "dreamlike",
-    ]:
-        if args.precision == "fp16":
-            vae = vae.half().cuda()
-            inputs = tuple(
-                [inputs.half().cuda() for inputs in model_input["v1_4"]["vae"]]
-            )
-        else:
-            inputs = model_input["v1_4"]["vae"]
-    else:
-        raise ValueError(f"{args.variant} not yet added")
-
-    shark_vae = compile_through_fx(
-        vae,
-        inputs,
-        model_name=model_name,
-        extra_args=extra_args,
-    )
-    return shark_vae
-
-
-def get_unet_mlir(model_name="unet", extra_args=[]):
-    class UnetModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.unet = UNet2DConditionModel.from_pretrained(
-                model_config[args.version]
-                if args.variant == "stablediffusion"
-                else model_variant[args.variant],
-                subfolder="unet",
-                revision=model_revision[args.variant],
-            )
-            self.in_channels = self.unet.in_channels
-            self.train(False)
-
-        def forward(self, latent, timestep, text_embedding, guidance_scale):
-            # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
-            latents = torch.cat([latent] * 2)
-            unet_out = self.unet.forward(
-                latents, timestep, text_embedding, return_dict=False
-            )[0]
-            noise_pred_uncond, noise_pred_text = unet_out.chunk(2)
-            noise_pred = noise_pred_uncond + guidance_scale * (
-                noise_pred_text - noise_pred_uncond
-            )
-            return noise_pred
-
-    unet = UnetModel()
-    if args.variant == "stablediffusion":
-        if args.precision == "fp16":
-            unet = unet.half().cuda()
-            inputs = tuple(
-                [
-                    inputs.half().cuda() if len(inputs.shape) != 0 else inputs
-                    for inputs in model_input[args.version]["unet"]
-                ]
-            )
-        else:
-            inputs = model_input[args.version]["unet"]
-    elif args.variant in [
-        "anythingv3",
-        "analogdiffusion",
-        "openjourney",
-        "dreamlike",
-    ]:
-        if args.precision == "fp16":
-            unet = unet.half().cuda()
-            inputs = tuple(
-                [
-                    inputs.half().cuda() if len(inputs.shape) != 0 else inputs
-                    for inputs in model_input["v1_4"]["unet"]
-                ]
-            )
-        else:
-            inputs = model_input["v1_4"]["unet"]
-    else:
-        raise ValueError(f"{args.variant} is not yet added")
-    shark_unet = compile_through_fx(
-        unet,
-        inputs,
-        model_name=model_name,
-        extra_args=extra_args,
-    )
-    return shark_unet

shark/examples/shark_inference/stable_diffusion/opt_params.py

@@ -1,115 +0,0 @@
-import sys
-from model_wrappers import (
-    get_base_vae_mlir,
-    get_vae_mlir,
-    get_unet_mlir,
-    get_clip_mlir,
-)
-from resources import models_db
-from stable_args import args
-from utils import get_shark_model
-
-BATCH_SIZE = len(args.prompts)
-if BATCH_SIZE != 1:
-    sys.exit("Only batch size 1 is supported.")
-
-
-def get_params(bucket_key, model_key):
-    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")
-
-    try:
-        bucket = models_db[0][bucket_key]
-        model_name = models_db[1][model_key]
-    except KeyError:
-        raise Exception(
-            f"{bucket}/{model_key} is not present in the models database"
-        )
-
-    return bucket, model_name, iree_flags
-
-
-def get_unet():
-    # Tuned model is present only for `fp16` precision.
-    is_tuned = "/tuned" if args.use_tuned else "/untuned"
-    bucket_key = f"{args.variant}{is_tuned}"
-    model_key = f"{args.variant}/{args.version}/unet/{args.precision}/length_{args.max_length}{is_tuned}"
-    bucket, model_name, iree_flags = get_params(bucket_key, model_key)
-    if args.use_tuned:
-        return get_shark_model(bucket, model_name, iree_flags)
-    else:
-        if args.precision == "fp16":
-            iree_flags += [
-                "--iree-flow-enable-padding-linalg-ops",
-                "--iree-flow-linalg-ops-padding-size=32",
-            ]
-            if args.device == "cuda":
-                iree_flags += [
-                    "--iree-flow-enable-conv-nchw-to-nhwc-transform"
-                ]
-            else:
-                iree_flags += ["--iree-flow-enable-conv-img2col-transform"]
-        elif args.precision == "fp32":
-            iree_flags += [
-                "--iree-flow-enable-conv-nchw-to-nhwc-transform",
-                "--iree-flow-enable-padding-linalg-ops",
-                "--iree-flow-linalg-ops-padding-size=16",
-            ]
-        if args.import_mlir:
-            return get_unet_mlir(model_name, iree_flags)
-        return get_shark_model(bucket, model_name, iree_flags)
-
-
-def get_vae():
-    # 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 ""
-    bucket_key = f"{args.variant}{is_tuned}"
-    model_key = f"{args.variant}/{args.version}/vae/{args.precision}/length_77{is_tuned}{is_base}"
-    bucket, model_name, iree_flags = get_params(bucket_key, model_key)
-    if args.use_tuned:
-        iree_flags += [
-            "--iree-flow-enable-padding-linalg-ops",
-            "--iree-flow-linalg-ops-padding-size=32",
-            "--iree-flow-enable-conv-img2col-transform",
-            "--iree-flow-enable-conv-winograd-transform",
-        ]
-        return get_shark_model(bucket, model_name, iree_flags)
-    else:
-        if args.precision == "fp16":
-            iree_flags += [
-                "--iree-flow-enable-padding-linalg-ops",
-                "--iree-flow-linalg-ops-padding-size=32",
-                "--iree-flow-enable-conv-img2col-transform",
-            ]
-        elif args.precision == "fp32":
-            iree_flags += [
-                "--iree-flow-enable-conv-nchw-to-nhwc-transform",
-                "--iree-flow-enable-padding-linalg-ops",
-                "--iree-flow-linalg-ops-padding-size=16",
-            ]
-        if args.import_mlir:
-            if args.use_base_vae:
-                return get_base_vae_mlir(model_name, iree_flags)
-            return get_vae_mlir(model_name, iree_flags)
-        return get_shark_model(bucket, model_name, iree_flags)
-
-
-def get_clip():
-    bucket_key = f"{args.variant}/untuned"
-    model_key = f"{args.variant}/{args.version}/clip/fp32/length_{args.max_length}/untuned"
-    bucket, model_name, iree_flags = get_params(bucket_key, model_key)
-    iree_flags += [
-        "--iree-flow-linalg-ops-padding-size=16",
-        "--iree-flow-enable-padding-linalg-ops",
-    ]
-    if args.import_mlir:
-        return get_clip_mlir(model_name, iree_flags)
-    return get_shark_model(bucket, model_name, iree_flags)

shark/examples/shark_inference/stable_diffusion/profiling_with_iree.md

@@ -1,44 +0,0 @@
-Compile / Run Instructions:
-
-To compile .vmfb for SD (vae, unet, CLIP), run the following commands with the .mlir in your local shark_tank cache (default location for Linux users is `~/.local/shark_tank`). These will be available once the script from [this README](https://github.com/nod-ai/SHARK/blob/main/shark/examples/shark_inference/stable_diffusion/README.md) is run once.
-Running the script mentioned above with the `--save_vmfb` flag will also save the .vmfb in your SHARK base directory if you want to skip straight to benchmarks.
-
-Compile Commands FP32/FP16: 
-
-```shell
-Vulkan AMD: 
-iree-compile --iree-input-type=none --iree-hal-target-backends=vulkan --iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
-
-#  add --mlir-print-debuginfo --mlir-print-op-on-diagnostic=true for debug
-#  use –iree-input-type=mhlo for tf models
-
-CUDA NVIDIA:
-iree-compile --iree-input-type=none --iree-hal-target-backends=cuda --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
-
-CPU:
-iree-compile --iree-input-type=none --iree-hal-target-backends=llvm-cpu  --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
-```
-
-
-
-Run / Benchmark Command (FP32 - NCHW):
-(NEED to use BS=2 since we do two forward passes to unet as a result of classifier free guidance.)
-
-```shell
-## Vulkan AMD:
-iree-benchmark-module --module_file=/path/to/output/vmfb --entry_function=forward --device=vulkan --function_input=1x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32 --function_input=f32=1.0 --function_input=f32=1.0
-
-## CUDA:
-iree-benchmark-module --module_file=/path/to/vmfb --entry_function=forward --device=cuda  --function_input=1x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32 --function_input=f32=1.0 --function_input=f32=1.0
-
-## CPU:
-iree-benchmark-module --module_file=/path/to/vmfb --entry_function=forward --device=local-task  --function_input=1x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32 --function_input=f32=1.0 --function_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_file=/path/to/unet.vmfb --function_input=1x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32 --function_input=f32=1.0 --function_input=f32=1.0
-```

shark/examples/shark_inference/stable_diffusion/resources.py

@@ -1,31 +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)
-
-
-prompt_examples = []
-prompts_loc = resource_path("resources/prompts.json")
-if os.path.exists(prompts_loc):
-    with open(prompts_loc, encoding="utf-8") as fopen:
-        prompt_examples = json.load(fopen)
-
-if not prompt_examples:
-    print("Unable to fetch prompt examples.")
-
-
-models_db = []
-models_loc = resource_path("resources/model_db.json")
-if os.path.exists(models_loc):
-    with open(models_loc, encoding="utf-8") as fopen:
-        models_db = json.load(fopen)
-
-if len(models_db) != 2:
-    sys.exit("Error: Unable to load models database.")

shark/examples/shark_inference/stable_diffusion/resources/model_db.json

@@ -1,68 +0,0 @@
-[
-  {
-    "stablediffusion/untuned":"gs://shark_tank/stable_diffusion",
-    "stablediffusion/tuned":"gs://shark_tank/sd_tuned",
-    "anythingv3/untuned":"gs://shark_tank/sd_anythingv3",
-    "anythingv3/tuned":"gs://shark_tank/sd_tuned",
-    "analogdiffusion/untuned":"gs://shark_tank/sd_analog_diffusion",
-    "analogdiffusion/tuned":"gs://shark_tank/sd_tuned",
-    "openjourney/untuned":"gs://shark_tank/sd_openjourney",
-    "openjourney/tuned":"gs://shark_tank/sd_tuned",
-    "dreamlike/untuned":"gs://shark_tank/sd_dreamlike_diffusion"
-  },
-  {
-    "stablediffusion/v1_4/unet/fp16/length_77/untuned":"unet_8dec_fp16",
-    "stablediffusion/v1_4/unet/fp16/length_77/tuned":"unet_1dec_fp16_tuned",
-    "stablediffusion/v1_4/unet/fp32/length_77/untuned":"unet_1dec_fp32",
-    "stablediffusion/v1_4/vae/fp16/length_77/untuned":"vae_19dec_fp16",
-    "stablediffusion/v1_4/vae/fp16/length_77/untuned/base":"vae_8dec_fp16",
-    "stablediffusion/v1_4/vae/fp32/length_77/untuned":"vae_1dec_fp32",
-    "stablediffusion/v1_4/clip/fp32/length_77/untuned":"clip_18dec_fp32",
-    "stablediffusion/v2_1base/unet/fp16/length_77/untuned":"unet2base_8dec_fp16",
-    "stablediffusion/v2_1base/unet/fp16/length_77/tuned":"unet2base_8dec_fp16_tuned_v2",
-    "stablediffusion/v2_1base/unet/fp16/length_64/untuned":"unet_19dec_v2p1base_fp16_64",
-    "stablediffusion/v2_1base/unet/fp16/length_64/tuned":"unet_19dec_v2p1base_fp16_64_tuned",
-    "stablediffusion/v2_1base/vae/fp16/length_77/untuned":"vae2base_19dec_fp16",
-    "stablediffusion/v2_1base/vae/fp16/length_77/tuned":"vae2base_19dec_fp16_tuned",
-    "stablediffusion/v2_1base/vae/fp16/length_77/untuned/base":"vae2base_8dec_fp16",
-    "stablediffusion/v2_1base/vae/fp16/length_77/tuned/base":"vae2base_8dec_fp16_tuned",
-    "stablediffusion/v2_1base/clip/fp32/length_77/untuned":"clip2base_18dec_fp32",
-    "stablediffusion/v2_1base/clip/fp32/length_64/untuned":"clip_19dec_v2p1base_fp32_64",
-    "stablediffusion/v2_1/unet/fp16/length_77/untuned":"unet2_14dec_fp16",
-    "stablediffusion/v2_1/vae/fp16/length_77/untuned":"vae2_19dec_fp16",
-    "stablediffusion/v2_1/vae/fp16/length_77/untuned/base":"vae2_8dec_fp16",
-    "stablediffusion/v2_1/clip/fp32/length_77/untuned":"clip2_18dec_fp32",
-    "anythingv3/v2_1base/unet/fp16/length_77/untuned":"av3_unet_19dec_fp16",
-    "anythingv3/v2_1base/unet/fp16/length_77/tuned":"av3_unet_19dec_fp16_tuned",
-    "anythingv3/v2_1base/unet/fp32/length_77/untuned":"av3_unet_19dec_fp32",
-    "anythingv3/v2_1base/vae/fp16/length_77/untuned":"av3_vae_19dec_fp16",
-    "anythingv3/v2_1base/vae/fp16/length_77/tuned":"av3_vae_19dec_fp16_tuned",
-    "anythingv3/v2_1base/vae/fp16/length_77/untuned/base":"av3_vaebase_22dec_fp16",
-    "anythingv3/v2_1base/vae/fp32/length_77/untuned":"av3_vae_19dec_fp32",
-    "anythingv3/v2_1base/vae/fp32/length_77/untuned/base":"av3_vaebase_22dec_fp32",
-    "anythingv3/v2_1base/clip/fp32/length_77/untuned":"av3_clip_19dec_fp32",
-    "analogdiffusion/v2_1base/unet/fp16/length_77/untuned":"ad_unet_19dec_fp16",
-    "analogdiffusion/v2_1base/unet/fp16/length_77/tuned":"ad_unet_19dec_fp16_tuned",
-    "analogdiffusion/v2_1base/unet/fp32/length_77/untuned":"ad_unet_19dec_fp32",
-    "analogdiffusion/v2_1base/vae/fp16/length_77/untuned":"ad_vae_19dec_fp16",
-    "analogdiffusion/v2_1base/vae/fp16/length_77/tuned":"ad_vae_19dec_fp16_tuned",
-    "analogdiffusion/v2_1base/vae/fp16/length_77/untuned/base":"ad_vaebase_22dec_fp16",
-    "analogdiffusion/v2_1base/vae/fp32/length_77/untuned":"ad_vae_19dec_fp32",
-    "analogdiffusion/v2_1base/vae/fp32/length_77/untuned/base":"ad_vaebase_22dec_fp32",
-    "analogdiffusion/v2_1base/clip/fp32/length_77/untuned":"ad_clip_19dec_fp32",
-    "openjourney/v2_1base/unet/fp16/length_64/untuned":"oj_unet_22dec_fp16_64",
-    "openjourney/v2_1base/unet/fp32/length_64/untuned":"oj_unet_22dec_fp32_64",
-    "openjourney/v2_1base/vae/fp16/length_77/untuned":"oj_vae_22dec_fp16",
-    "openjourney/v2_1base/vae/fp16/length_77/untuned/base":"oj_vaebase_22dec_fp16",
-    "openjourney/v2_1base/vae/fp32/length_77/untuned":"oj_vae_22dec_fp32",
-    "openjourney/v2_1base/vae/fp32/length_77/untuned/base":"oj_vaebase_22dec_fp32",
-    "openjourney/v2_1base/clip/fp32/length_64/untuned":"oj_clip_22dec_fp32_64",
-    "dreamlike/v2_1base/unet/fp16/length_77/untuned":"dl_unet_23dec_fp16_77",
-    "dreamlike/v2_1base/unet/fp32/length_77/untuned":"dl_unet_23dec_fp32_77",
-    "dreamlike/v2_1base/vae/fp16/length_77/untuned":"dl_vae_23dec_fp16",
-    "dreamlike/v2_1base/vae/fp16/length_77/untuned/base":"dl_vaebase_23dec_fp16",
-    "dreamlike/v2_1base/vae/fp32/length_77/untuned":"dl_vae_23dec_fp32",
-    "dreamlike/v2_1base/vae/fp32/length_77/untuned/base":"dl_vaebase_23dec_fp32",
-    "dreamlike/v2_1base/clip/fp32/length_77/untuned":"dl_clip_23dec_fp32_77"
-  }
-]

shark/examples/shark_inference/stable_diffusion/schedulers.py

@@ -1,131 +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 utils import compile_through_fx, get_shark_model
-from stable_args import args
-import torch
-
-SCHEDULER_BUCKET = "gs://shark_tank/stable_diffusion/schedulers"
-
-model_input = {
-    "euler": {
-        "latent": torch.randn(1, 4, 64, 64),
-        "output": torch.randn(1, 4, 64, 64),
-        "sigma": torch.tensor(1).to(torch.float32),
-        "dt": torch.tensor(1).to(torch.float32),
-    },
-}
-
-
-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):
-        example_latent = model_input["euler"]["latent"]
-        example_output = model_input["euler"]["output"]
-        if args.precision == "fp16":
-            example_latent = example_latent.half()
-            example_output = example_output.half()
-        example_sigma = model_input["euler"]["sigma"]
-        example_dt = model_input["euler"]["dt"]
-
-        class ScalingModel(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-
-            def forward(self, latent, sigma):
-                return latent / ((sigma**2 + 1) ** 0.5)
-
-        class SchedulerStepModel(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-
-            def forward(self, noise_pred, sigma, latent, dt):
-                pred_original_sample = latent - sigma * noise_pred
-                derivative = (latent - pred_original_sample) / sigma
-                return latent + derivative * dt
-
-        iree_flags = []
-        if len(args.iree_vulkan_target_triple) > 0:
-            iree_flags.append(
-                f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
-            )
-        # Disable bindings fusion to work with moltenVK.
-        if sys.platform == "darwin":
-            iree_flags.append("-iree-stream-fuse-binding=false")
-
-        if args.import_mlir:
-            scaling_model = ScalingModel()
-            self.scaling_model = compile_through_fx(
-                scaling_model,
-                (example_latent, example_sigma),
-                model_name="euler_scale_model_input_" + args.precision,
-                extra_args=iree_flags,
-            )
-
-            step_model = SchedulerStepModel()
-            self.step_model = compile_through_fx(
-                step_model,
-                (example_output, example_sigma, example_latent, example_dt),
-                model_name="euler_step_" + args.precision,
-                extra_args=iree_flags,
-            )
-        else:
-            self.scaling_model = get_shark_model(
-                SCHEDULER_BUCKET,
-                "euler_scale_model_input_" + args.precision,
-                iree_flags,
-            )
-            self.step_model = get_shark_model(
-                SCHEDULER_BUCKET, "euler_step_" + args.precision, iree_flags
-            )
-
-    def scale_model_input(self, sample, timestep):
-        step_index = (self.timesteps == timestep).nonzero().item()
-        sigma = self.sigmas[step_index]
-        return self.scaling_model.forward(
-            (
-                sample,
-                sigma,
-            ),
-            send_to_host=False,
-        )
-
-    def step(self, noise_pred, timestep, latent):
-        step_index = (self.timesteps == timestep).nonzero().item()
-        sigma = self.sigmas[step_index]
-        dt = self.sigmas[step_index + 1] - sigma
-        return self.step_model.forward(
-            (
-                noise_pred,
-                sigma,
-                latent,
-                dt,
-            ),
-            send_to_host=False,
-        )

shark/examples/shark_inference/stable_diffusion/stable_diffusion_amd.md

@@ -1,139 +0,0 @@
-# Stable Diffusion optimized for AMD RDNA2/RDNA3 GPUs
-
-Before you start, please be aware that this is beta software that relies on a special AMD driver. Like all StableDiffusion GUIs published so far, you need some technical expertise to set it up. We apologize in advance if you bump into issues. If that happens, please don't hesitate to ask our Discord community for help! If you still can't get it to work, we're sorry, and please be assured that we (Nod and AMD) are working hard to improve the user experience in coming months.
-If it works well for you, please "star" the following GitHub projects... this is one of the best ways to help and spread the word!
-
-* https://github.com/nod-ai/SHARK
-* https://github.com/iree-org/iree
-
-## Install this specific AMD Drivers (AMD latest may not have all the fixes).
-
-### AMD KB Drivers for RDNA2 and RDNA3:
-
-*AMD Software: Adrenalin Edition 22.11.1 for MLIR/IREE Driver Version 22.20.29.09 for Windows® 10 and Windows® 11 (Windows Driver Store Version 31.0.12029.9003)*
-
-First, download this special driver in a folder of your choice. We recommend you keep that driver around since you may need to re-install it later, if Windows Update decides to overwrite it:
-https://www.amd.com/en/support/kb/release-notes/rn-rad-win-22-11-1-mlir-iree
-
-KNOWN ISSUES with this special AMD driver:
-* `Windows Update` may (depending how it's configured) automatically install a new official AMD driver that overwrites this IREE-specific driver. If Stable Diffusion used to work, then a few days later, it slows down a lot or produces incorrect results (e.g. black images), this may be the cause. To fix this problem, please check the installed driver's version, and re-install the special driver if needed. (TODO: document how to prevent this `Windows Update` behavior!)
-* Some people using this special driver experience mouse pointer accuracy issues, if you use a larger-than-default mouse pointer. The clicked point isn't centered properly. One possible work-around is to reset the pointer size to "1" in "Change pointer size and color".
-
-## Installation
-
-Download the latest Windows SHARK SD binary [here](https://github.com/nod-ai/SHARK/releases/download/20221227.417/shark_sd_20221227_417.exe) in a folder of your choice. Please read carefully the following notes:
-
-Notes:
-* We recommend that you download this EXE in a new folder, whenever you download a new EXE version. If you download it in the same folder as a previous install, you must delete the old `*.vmfb` files. Those contain Vulkan dispatches compiled from MLIR, that can get outdated if you run multiple EXE from the same folder. You can use `--clean_all` flag once to clean all the old files. 
-* Your browser may warn you about downloading an .exe file
-* If you recently updated the driver or this binary (EXE file), we recommend you:
-  * clear all the local artifacts with `--clean_all` OR 
-  * clear the Vulkan shader cache: For Windows users this can be done by clearing the contents of `C:\Users\<username>\AppData\Local\AMD\VkCache\`. On Linux the same cache is typically located at `~/.cache/AMD/VkCache/`.
-  * clear the `huggingface` cache. In Windows, this is `C:\Users\<username>\.cache\huggingface`.
-
-## Running
-
-* Open a Command Prompt or Powershell terminal, change folder (`cd`) to the .exe folder. Then run the EXE from the command prompt. That way, if an error occurs, you'll be able to cut-and-paste it to ask for help. (if it always works for you without error, you may simply double-click the EXE to start the web browser)
-* The first run may take about 10-15 minutes when the models are downloaded and compiled. Your patience is appreciated. The download could be about 5GB.
-* If successful, you will likely see a Windows Defender message asking you to give permission to open a web server port. Accept it.
-* Open a browser to access the Stable Diffusion web server. By default, the port is 8080, so you can go to http://localhost:8080/?__theme=dark.
-
-## Stopping
-
-* Select the command prompt that's running the EXE. Press CTRL-C and wait a moment. The application should stop. 
-* Please make sure to do the above step before you attempt to update the EXE to a new version.
-
-# Results
-
-<img width="1607" alt="webui" src="https://user-images.githubusercontent.com/74956/204939260-b8308bc2-8dc4-47f6-9ac0-f60b66edab99.png">
-
-
-Here are some samples generated:
-
-![tajmahal, snow, sunflowers, oil on canvas_0](https://user-images.githubusercontent.com/74956/204934186-141f7e43-6eb2-4e89-a99c-4704d20444b3.jpg)
-
-![a photo of a crab playing a trumpet](https://user-images.githubusercontent.com/74956/204933258-252e7240-8548-45f7-8253-97647d38313d.jpg)
-
-
-<details>
-  <summary>Advanced Installation </summary>
-
-
-## Setup your Python VirtualEnvironment and Dependencies
-
-### Windows 10/11 Users
-
-* Install the latest Python 3.10.x version from [here](https://www.python.org/downloads/windows/)
-
-* Install Git for Windows from [here](https://git-scm.com/download/win)
-
-#### Allow the install script to run in Powershell
-```powershell
-set-executionpolicy remotesigned 
-```
-
-#### Setup venv and install necessary packages (torch-mlir, nodLabs/Shark, ...)
-```powershell
-git clone https://github.com/nod-ai/SHARK.git
-cd SHARK
-./setup_venv.ps1 #You can re-run this script to get the latest version
-```
-
-### Linux
-
-```shell
-git clone https://github.com/nod-ai/SHARK.git
-cd SHARK
-./setup_venv.sh
-source shark.venv/bin/activate
-```
-
-### Run Stable Diffusion on your device - WebUI
-
-#### Windows 10/11 Users
-```powershell
-(shark.venv) PS C:\Users\nod\SHARK> cd web
-(shark.venv) PS C:\Users\nod\SHARK\web> python index.py
-```
-#### Linux Users
-```shell
-(shark.venv) > cd web
-(shark.venv) > python index.py
-```
-
-
-
-### Run Stable Diffusion on your device - Commandline
-
-#### Windows 10/11 Users
-```powershell
-(shark.venv) PS C:\g\shark> python .\shark\examples\shark_inference\stable_diffusion\main.py --precision="fp16" --prompt="tajmahal, snow, sunflowers, oil on canvas" --device="vulkan"
-```
-
-#### Linux
-```shell
-python3.10 shark/examples/shark_inference/stable_diffusion/main.py --precision=fp16 --device=vulkan --prompt="tajmahal, oil on canvas, sunflowers, 4k, uhd"
-```
-
-The output on a 6900XT would like:
-
-```shell 
-44it [00:08,  5.14it/s]i = 44 t = 120 (191ms)
-45it [00:08,  5.15it/s]i = 45 t = 100 (191ms)
-46it [00:08,  5.16it/s]i = 46 t = 80 (191ms)
-47it [00:09,  5.16it/s]i = 47 t = 60 (193ms)
-48it [00:09,  5.15it/s]i = 48 t = 40 (195ms)
-49it [00:09,  5.12it/s]i = 49 t = 20 (196ms)
-50it [00:09,  5.14it/s]
-Average step time: 192.8154182434082ms/it
-Total image generation runtime (s): 10.390909433364868
-(shark.venv) PS C:\g\shark>
-```
-
-
-For more options to the Stable Diffusion model read [this](https://github.com/nod-ai/SHARK/blob/main/shark/examples/shark_inference/stable_diffusion/README.md)
-</details>
-<details>
-  <summary>Discord link</summary>
-Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any trouble with running it on your hardware. 
-</details>

shark/examples/shark_inference/upscaler/main.py

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

shark/examples/shark_inference/upscaler/model_wrappers.py

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

shark/examples/shark_inference/upscaler/opt_params.py

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

shark/examples/shark_inference/upscaler/pipeline_shark_stable_diffusion_upscale.py

@@ -0,0 +1,489 @@
+import inspect
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+
+import PIL
+from PIL import Image
+from diffusers.utils import is_accelerate_available
+from transformers import CLIPTextModel, CLIPTokenizer
+from diffusers import AutoencoderKL, UNet2DConditionModel
+from diffusers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers import logging
+from diffusers.pipeline_utils import ImagePipelineOutput
+from opt_params import get_unet, get_vae, get_clip
+from tqdm.auto import tqdm
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def preprocess(image):
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [image]
+
+    if isinstance(image[0], PIL.Image.Image):
+        w, h = image[0].size
+        w, h = map(
+            lambda x: x - x % 64, (w, h)
+        )  # resize to integer multiple of 64
+
+        image = [np.array(i.resize((w, h)))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
+
+
+def shark_run_wrapper(model, *args):
+    np_inputs = tuple([x.detach().numpy() for x in args])
+    outputs = model("forward", np_inputs)
+    return torch.from_numpy(outputs)
+
+
+class SharkStableDiffusionUpscalePipeline:
+    def __init__(
+        self,
+        model_id,
+    ):
+        self.tokenizer = CLIPTokenizer.from_pretrained(
+            model_id, subfolder="tokenizer"
+        )
+        self.low_res_scheduler = DDPMScheduler.from_pretrained(
+            model_id,
+            subfolder="scheduler",
+        )
+        self.scheduler = DDIMScheduler.from_pretrained(
+            model_id,
+            subfolder="scheduler",
+        )
+        self.vae = get_vae()
+        self.unet = get_unet()
+        self.text_encoder = get_clip()
+        self.max_noise_level = (350,)
+        self._execution_device = "cpu"
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(
+            prompt, padding="longest", return_tensors="pt"
+        ).input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[
+            -1
+        ] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(
+                untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+            )
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        # if (
+        # hasattr(self.text_encoder.config, "use_attention_mask")
+        # and self.text_encoder.config.use_attention_mask
+        # ):
+        # attention_mask = text_inputs.attention_mask.to(device)
+        # else:
+        # attention_mask = None
+
+        text_embeddings = shark_run_wrapper(
+            self.text_encoder, text_input_ids.to(device)
+        )
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(
+            bs_embed * num_images_per_prompt, seq_len, -1
+        )
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            # if (
+            # hasattr(self.text_encoder.config, "use_attention_mask")
+            # and self.text_encoder.config.use_attention_mask
+            # ):
+            # attention_mask = uncond_input.attention_mask.to(device)
+            # else:
+            # attention_mask = None
+
+            uncond_embeddings = shark_run_wrapper(
+                self.text_encoder,
+                uncond_input.input_ids.to(device),
+            )
+            uncond_embeddings = uncond_embeddings
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(
+                1, num_images_per_prompt, 1
+            )
+            uncond_embeddings = uncond_embeddings.view(
+                batch_size * num_images_per_prompt, seq_len, -1
+            )
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents with 0.18215->0.08333
+    def decode_latents(self, latents):
+        latents = 1 / 0.08333 * latents
+        image = shark_run_wrapper(self.vae, latents)
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def check_inputs(self, prompt, image, noise_level, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(
+                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
+            )
+
+        if (
+            not isinstance(image, torch.Tensor)
+            and not isinstance(image, PIL.Image.Image)
+            and not isinstance(image, list)
+        ):
+            raise ValueError(
+                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}"
+            )
+
+        # verify batch size of prompt and image are same if image is a list or tensor
+        if isinstance(image, list) or isinstance(image, torch.Tensor):
+            if isinstance(prompt, str):
+                batch_size = 1
+            else:
+                batch_size = len(prompt)
+            if isinstance(image, list):
+                image_batch_size = len(image)
+            else:
+                image_batch_size = image.shape[0]
+            if batch_size != image_batch_size:
+                raise ValueError(
+                    f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}."
+                    " Please make sure that passed `prompt` matches the batch size of `image`."
+                )
+
+    @staticmethod
+    def numpy_to_pil(images):
+        """
+        Convert a numpy image or a batch of images to a PIL image.
+        """
+        if images.ndim == 3:
+            images = images[None, ...]
+        images = (images * 255).round().astype("uint8")
+        if images.shape[-1] == 1:
+            # special case for grayscale (single channel) images
+            pil_images = [
+                Image.fromarray(image.squeeze(), mode="L") for image in images
+            ]
+        else:
+            pil_images = [Image.fromarray(image) for image in images]
+
+        return pil_images
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        shape = (batch_size, num_channels_latents, height, width)
+        if latents is None:
+            if device == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(
+                    shape, generator=generator, device="cpu", dtype=dtype
+                ).to(device)
+            else:
+                latents = torch.randn(
+                    shape, generator=generator, device=device, dtype=dtype
+                )
+        else:
+            if latents.shape != shape:
+                raise ValueError(
+                    f"Unexpected latents shape, got {latents.shape}, expected {shape}"
+                )
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[
+            torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]
+        ],
+        num_inference_steps: int = 75,
+        guidance_scale: float = 9.0,
+        noise_level: int = 20,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[
+            Union[torch.Generator, List[torch.Generator]]
+        ] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[
+            Callable[[int, int, torch.FloatTensor], None]
+        ] = None,
+        callback_steps: Optional[int] = 1,
+    ):
+        # 1. Check inputs
+        self.check_inputs(prompt, image, noise_level, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+        )
+
+        # 4. Preprocess image
+        image = preprocess(image)
+        image = image.to(dtype=text_embeddings.dtype, device=device)
+
+        # 5. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Add noise to image
+        noise_level = torch.tensor(
+            [noise_level], dtype=torch.long, device=device
+        )
+        if device == "mps":
+            # randn does not work reproducibly on mps
+            noise = torch.randn(
+                image.shape,
+                generator=generator,
+                device="cpu",
+                dtype=text_embeddings.dtype,
+            ).to(device)
+        else:
+            noise = torch.randn(
+                image.shape,
+                generator=generator,
+                device=device,
+                dtype=text_embeddings.dtype,
+            )
+        image = self.low_res_scheduler.add_noise(image, noise, noise_level)
+
+        batch_multiplier = 2 if do_classifier_free_guidance else 1
+        image = torch.cat([image] * batch_multiplier * num_images_per_prompt)
+        noise_level = torch.cat([noise_level] * image.shape[0])
+
+        # 6. Prepare latent variables
+        height, width = image.shape[2:]
+        # num_channels_latents = self.vae.config.latent_channels
+        num_channels_latents = 4
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            text_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 7. Check that sizes of image and latents match
+        num_channels_image = image.shape[1]
+        # if (
+        # num_channels_latents + num_channels_image
+        # != self.unet.config.in_channels
+        # ):
+        # raise ValueError(
+        # f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
+        # f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+        # f" `num_channels_image`: {num_channels_image} "
+        # f" = {num_channels_latents+num_channels_image}. Please verify the config of"
+        # " `pipeline.unet` or your `image` input."
+        # )
+
+        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 9. Denoising loop
+        num_warmup_steps = (
+            len(timesteps) - num_inference_steps * self.scheduler.order
+        )
+        for i, t in tqdm(enumerate(timesteps)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = (
+                torch.cat([latents] * 2)
+                if do_classifier_free_guidance
+                else latents
+            )
+
+            # concat latents, mask, masked_image_latents in the channel dimension
+            latent_model_input = self.scheduler.scale_model_input(
+                latent_model_input, t
+            )
+            latent_model_input = torch.cat([latent_model_input, image], dim=1)
+
+            timestep = torch.tensor([t]).to(torch.float32)
+
+            # predict the noise residual
+            noise_pred = shark_run_wrapper(
+                self.unet,
+                latent_model_input.half(),
+                timestep,
+                text_embeddings.half(),
+                noise_level,
+            )
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (
+                noise_pred_text - noise_pred_uncond
+            )
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(
+                noise_pred, t, latents, **extra_step_kwargs
+            ).prev_sample
+
+            # # call the callback, if provided
+            # if i == len(timesteps) - 1 or (
+            # (i + 1) > num_warmup_steps
+            # and (i + 1) % self.scheduler.order == 0
+            # ):
+            # progress_bar.update()
+            # if callback is not None and i % callback_steps == 0:
+            # callback(i, t, latents)
+
+        # 10. Post-processing
+        # make sure the VAE is in float32 mode, as it overflows in float16
+        # self.vae.to(dtype=torch.float32)
+        image = self.decode_latents(latents.float())
+
+        # 11. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

shark/examples/shark_inference/upscaler/upscaler_args.py

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

shark/examples/shark_inference/upscaler/utils.py

@@ -1,7 +1,7 @@
 import os
 import torch
 from shark.shark_inference import SharkInference
-from stable_args import args
+from upscaler_args import args
 from shark.shark_importer import import_with_fx
 from shark.iree_utils.vulkan_utils import (
     set_iree_vulkan_runtime_flags,
@@ -45,7 +45,7 @@ def get_shark_model(tank_url, model_name, extra_args=[]):
     from shark.parser import shark_args
 
     # Set local shark_tank cache directory.
-    shark_args.local_tank_cache = args.local_tank_cache
+    # shark_args.local_tank_cache = args.local_tank_cache
 
     mlir_model, func_name, inputs, golden_out = download_model(
         model_name,
@@ -53,19 +53,20 @@ def get_shark_model(tank_url, model_name, extra_args=[]):
         frontend="torch",
     )
     shark_module = SharkInference(
-        mlir_model, func_name, device=args.device, mlir_dialect="linalg"
+        mlir_model, device=args.device, mlir_dialect="linalg"
     )
     return _compile_module(shark_module, model_name, extra_args)
 
 
 # Converts the torch-module into a shark_module.
-def compile_through_fx(model, inputs, model_name, extra_args=[]):
-
-    mlir_module, func_name = import_with_fx(model, inputs)
-
+def compile_through_fx(
+    model, inputs, model_name, is_f16=False, f16_input_mask=None, extra_args=[]
+):
+    mlir_module, func_name = import_with_fx(
+        model, inputs, is_f16, f16_input_mask
+    )
     shark_module = SharkInference(
         mlir_module,
-        func_name,
         device=args.device,
         mlir_dialect="linalg",
     )
@@ -74,7 +75,6 @@ def compile_through_fx(model, inputs, model_name, extra_args=[]):
 
 
 def set_iree_runtime_flags():
-
     vulkan_runtime_flags = [
         f"--vulkan_large_heap_block_size={args.vulkan_large_heap_block_size}",
         f"--vulkan_validation_layers={'true' if args.vulkan_validation_layers else 'false'}",

shark/examples/shark_training/bert_training.py

@@ -1,7 +1,7 @@
 import torch
 from torch.nn.utils import _stateless
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from shark.shark_runner import SharkTrainer
+from shark.shark_trainer import SharkTrainer
 
 
 class MiniLMSequenceClassification(torch.nn.Module):
@@ -42,6 +42,7 @@ def forward(params, buffers, args):
     return params, buffers
 
 
-shark_module = SharkTrainer(mod, inp, custom_inference_fn=forward)
+shark_module = SharkTrainer(mod, inp)
+shark_module.compile(forward)
 
-print(shark_module.forward())
+print(shark_module.train())

shark/examples/shark_training/stable-diffusion-img2img/stable_diffusion_img2img.py

@@ -169,6 +169,7 @@ imagenet_style_templates_small = [
     "a large painting in the style of {}",
 ]
 
+
 # Setup the dataset
 class TextualInversionDataset(Dataset):
     def __init__(
@@ -184,7 +185,6 @@ class TextualInversionDataset(Dataset):
         placeholder_token="*",
         center_crop=False,
     ):
-
         self.data_root = data_root
         self.tokenizer = tokenizer
         self.learnable_property = learnable_property
@@ -244,7 +244,10 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            h, w, = (
+            (
+                h,
+                w,
+            ) = (
                 img.shape[0],
                 img.shape[1],
             )

shark/iree_eager_backend.py

@@ -21,7 +21,6 @@ import torch
 from iree.runtime import DeviceArray
 from torch_mlir._mlir_libs._mlir.ir import Module
 from torch_mlir.compiler_utils import (
-    get_module_name_for_debug_dump,
     run_pipeline_with_repro_report,
 )
 from torch_mlir.eager_mode.torch_mlir_eager_backend import (
@@ -64,14 +63,13 @@ class EagerModeIREELinalgOnTensorsBackend(TorchMLIREagerBackend):
         )
 
     def compile(self, imported_module: Module):
-        fn_name = get_module_name_for_debug_dump(imported_module)
         run_pipeline_with_repro_report(
             imported_module,
             "torch-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline",
             "EagerMode",
         )
         callable, _ = get_iree_compiled_module(
-            imported_module, self.raw_device_str, func_name=fn_name
+            imported_module, self.raw_device_str
         )
         return callable
 

shark/iree_utils/_common.py

@@ -33,8 +33,9 @@ def run_cmd(cmd):
         )
         result_str = result.stdout.decode()
         return result_str
-    except Exception:
-        sys.exit("Exiting program due to error running:", cmd)
+    except subprocess.CalledProcessError as e:
+        print(e.output)
+        sys.exit(f"Exiting program due to error running {cmd}")
 
 
 def iree_device_map(device):

shark/iree_utils/benchmark_utils.py

@@ -18,6 +18,7 @@ from shark.iree_utils.cpu_utils import get_cpu_count
 import numpy as np
 import os
 import re
+import platform
 
 UNIT_TO_SECOND_MAP = {"us": 1e-6, "ms": 0.001, "s": 1}
 
@@ -62,24 +63,33 @@ def build_benchmark_args(
     Outputs: string that execute benchmark-module on target model.
     """
     path = benchmark_module.__path__[0]
-    benchmarker_path = os.path.join(path, "..", "..", "iree-benchmark-module")
-    benchmark_cl = [benchmarker_path, f"--module_file={input_file}"]
+    if platform.system() == "Windows":
+        benchmarker_path = os.path.join(
+            path, "..", "..", "iree-benchmark-module.exe"
+        )
+        time_extractor = None
+    else:
+        benchmarker_path = os.path.join(
+            path, "..", "..", "iree-benchmark-module"
+        )
+        time_extractor = "| awk 'END{{print $2 $3}}'"
+    benchmark_cl = [benchmarker_path, f"--module={input_file}"]
     # TODO: The function named can be passed as one of the args.
     fn_name = "forward"
     if training == True:
         # TODO: Replace name of train with actual train fn name.
         fn_name = "train"
-    benchmark_cl.append(f"--entry_function={fn_name}")
+    benchmark_cl.append(f"--function={fn_name}")
     benchmark_cl.append(f"--device={iree_device_map(device)}")
     mlir_input_types = tensor_to_type_str(input_tensors, mlir_dialect)
     for mlir_input in mlir_input_types:
-        benchmark_cl.append(f"--function_input={mlir_input}")
+        benchmark_cl.append(f"--input={mlir_input}")
     if device == "cpu":
         num_cpus = get_cpu_count()
         if num_cpus is not None:
             benchmark_cl.append(f"--task_topology_max_group_count={num_cpus}")
-    time_extractor = "| awk 'END{{print $2 $3}}'"
-    benchmark_cl.append(time_extractor)
+    # if time_extractor:
+    #    benchmark_cl.append(time_extractor)
     return benchmark_cl
 
 
@@ -96,16 +106,24 @@ def build_benchmark_args_non_tensor_input(
     Outputs: string that execute benchmark-module on target model.
     """
     path = benchmark_module.__path__[0]
-    benchmarker_path = os.path.join(path, "..", "..", "iree-benchmark-module")
-    benchmark_cl = [benchmarker_path, f"--module_file={input_file}"]
+    if platform.system() == "Windows":
+        benchmarker_path = os.path.join(
+            path, "..", "..", "iree-benchmark-module.exe"
+        )
+    else:
+        benchmarker_path = os.path.join(
+            path, "..", "..", "iree-benchmark-module"
+        )
+    benchmark_cl = [benchmarker_path, f"--module={input_file}"]
     # TODO: The function named can be passed as one of the args.
     if function_name:
-        benchmark_cl.append(f"--entry_function={function_name}")
+        benchmark_cl.append(f"--function={function_name}")
     benchmark_cl.append(f"--device={iree_device_map(device)}")
     for input in inputs:
-        benchmark_cl.append(f"--function_input={input}")
-    time_extractor = "| awk 'END{{print $2 $3}}'"
-    benchmark_cl.append(time_extractor)
+        benchmark_cl.append(f"--input={input}")
+    if platform.system() != "Windows":
+        time_extractor = "| awk 'END{{print $2 $3}}'"
+        benchmark_cl.append(time_extractor)
     return benchmark_cl
 
 
@@ -121,8 +139,14 @@ def run_benchmark_module(benchmark_cl):
         benchmark_path
     ), "Cannot find benchmark_module, Please contact SHARK maintainer on discord."
     bench_result = run_cmd(" ".join(benchmark_cl))
-    regex_split = re.compile("([0-9]+[.]*[0-9]*)([a-zA-Z]+)")
-    match = regex_split.match(bench_result)
-    time = float(match.group(1))
-    unit = match.group(2)
-    return 1.0 / (time * UNIT_TO_SECOND_MAP[unit])
+    try:
+        regex_split = re.compile("(\d+[.]*\d*)(  *)([a-zA-Z]+)")
+        match = regex_split.search(bench_result)
+        time = float(match.group(1))
+        unit = match.group(3)
+    except AttributeError:
+        regex_split = re.compile("(\d+[.]*\d*)([a-zA-Z]+)")
+        match = regex_split.search(bench_result)
+        time = float(match.group(1))
+        unit = match.group(2)
+    return 1.0 / (time * 0.001)

shark/iree_utils/compile_utils.py

@@ -80,7 +80,17 @@ def get_iree_common_args():
 def get_model_specific_args():
     ms_args = []
     if shark_args.enable_conv_transform == True:
-        ms_args += ["--iree-flow-enable-conv-nchw-to-nhwc-transform"]
+        ms_args += [
+            "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-convert-conv-nchw-to-nhwc))"
+        ]
+    if shark_args.enable_img2col_transform == True:
+        ms_args += [
+            "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-preprocessing-convert-conv2d-to-img2col))"
+        ]
+    if shark_args.use_winograd == True:
+        ms_args += [
+            "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-linalg-ext-convert-conv2d-to-winograd))"
+        ]
     return ms_args
 
 
@@ -143,7 +153,6 @@ def compile_benchmark_dirs(bench_dir, device, dispatch_benchmarks):
                     in_dispatches = True
             if all_dispatches or in_dispatches:
                 for f_ in os.listdir(f"{bench_dir}/{d_}"):
-
                     if "benchmark.mlir" in f_:
                         dispatch_file = open(f"{bench_dir}/{d_}/{f_}", "r")
                         module = dispatch_file.read()
@@ -234,7 +243,6 @@ def compile_module_to_flatbuffer(
     module,
     device,
     frontend,
-    func_name,
     model_config_path,
     extra_args,
     model_name="None",
@@ -277,15 +285,25 @@ def compile_module_to_flatbuffer(
     return flatbuffer_blob
 
 
-def get_iree_module(flatbuffer_blob, device, func_name):
+def get_iree_module(flatbuffer_blob, device, device_idx=None):
     # Returns the compiled module and the configs.
-    config = get_iree_runtime_config(device)
+    if device_idx is not None:
+        device = iree_device_map(device)
+        print("registering device id: ", device_idx)
+        haldriver = ireert.get_driver(device)
+
+        haldevice = haldriver.create_device(
+            haldriver.query_available_devices()[device_idx]["device_id"]
+        )
+        config = ireert.Config(device=haldevice)
+    else:
+        config = get_iree_runtime_config(device)
     vm_module = ireert.VmModule.from_flatbuffer(
         config.vm_instance, flatbuffer_blob
     )
     ctx = ireert.SystemContext(config=config)
     ctx.add_vm_module(vm_module)
-    ModuleCompiled = ctx.modules.module[func_name]
+    ModuleCompiled = ctx.modules.module
     return ModuleCompiled, config
 
 
@@ -293,25 +311,22 @@ def get_iree_compiled_module(
     module,
     device: str,
     frontend: str = "torch",
-    func_name: str = "forward",
     model_config_path: str = None,
     extra_args: list = [],
+    device_idx: int = None,
 ):
     """Given a module returns the compiled .vmfb and configs"""
     flatbuffer_blob = compile_module_to_flatbuffer(
-        module, device, frontend, func_name, model_config_path, extra_args
+        module, device, frontend, model_config_path, extra_args
     )
-    return get_iree_module(flatbuffer_blob, device, func_name)
+    return get_iree_module(flatbuffer_blob, device, device_idx=device_idx)
 
 
-def load_flatbuffer(
-    flatbuffer_path: str, device: str, func_name: str = "forward"
-):
-
+def load_flatbuffer(flatbuffer_path: str, device: str, device_idx: int = None):
     with open(os.path.join(flatbuffer_path), "rb") as f:
         flatbuffer_blob = f.read()
 
-    return get_iree_module(flatbuffer_blob, device, func_name)
+    return get_iree_module(flatbuffer_blob, device, device_idx=device_idx)
 
 
 def export_iree_module_to_vmfb(
@@ -319,20 +334,19 @@ def export_iree_module_to_vmfb(
     device: str,
     directory: str,
     mlir_dialect: str = "linalg",
-    func_name: str = "forward",
     model_config_path: str = None,
     module_name: str = None,
     extra_args: list = [],
 ):
     # Compiles the module given specs and saves it as .vmfb file.
     flatbuffer_blob = compile_module_to_flatbuffer(
-        module, device, mlir_dialect, func_name, model_config_path, extra_args
+        module, device, mlir_dialect, model_config_path, extra_args
     )
     if module_name is None:
         device_name = (
             device if "://" not in device else "-".join(device.split("://"))
         )
-        module_name = f"{mlir_dialect}_{func_name}_{device_name}"
+        module_name = f"{mlir_dialect}_{device_name}"
     filename = os.path.join(directory, module_name + ".vmfb")
     print(f"Saved vmfb in {filename}.")
     with open(filename, "wb") as f:
@@ -355,11 +369,16 @@ def export_module_to_mlir_file(module, frontend, directory: str):
 
 
 def get_results(
-    compiled_vm, input, config, frontend="torch", send_to_host=True
+    compiled_vm,
+    function_name,
+    input,
+    config,
+    frontend="torch",
+    send_to_host=True,
 ):
     """Runs a .vmfb file given inputs and config and returns output."""
     device_inputs = [ireert.asdevicearray(config.device, a) for a in input]
-    result = compiled_vm(*device_inputs)
+    result = compiled_vm[function_name](*device_inputs)
     result_tensors = []
     if isinstance(result, tuple):
         if send_to_host:
@@ -376,7 +395,7 @@ def get_results(
             return np.copy(res)
         return data
     else:
-        if send_to_host:
+        if send_to_host and result is not None:
             return result.to_host()
         return result
 

shark/iree_utils/cpu_utils.py

@@ -15,6 +15,7 @@
 # All the iree_cpu related functionalities go here.
 
 import subprocess
+import platform
 
 
 def get_cpu_count():
@@ -29,25 +30,16 @@ def get_cpu_count():
 
 # Get the default cpu args.
 def get_iree_cpu_args():
-    find_triple_cmd = "uname -s -m"
-    os_name, proc_name = (
-        subprocess.run(
-            find_triple_cmd, shell=True, stdout=subprocess.PIPE, check=True
-        )
-        .stdout.decode("utf-8")
-        .split()
-    )
+    uname = platform.uname()
+    os_name, proc_name = uname.system, uname.machine
+
     if os_name == "Darwin":
-        find_kernel_version_cmd = "uname -r"
-        kernel_version = subprocess.run(
-            find_kernel_version_cmd,
-            shell=True,
-            stdout=subprocess.PIPE,
-            check=True,
-        ).stdout.decode("utf-8")
+        kernel_version = uname.release
         target_triple = f"{proc_name}-apple-darwin{kernel_version}"
     elif os_name == "Linux":
         target_triple = f"{proc_name}-linux-gnu"
+    elif os_name == "Windows":
+        target_triple = "x86_64-pc-windows-msvc"
     else:
         error_message = f"OS Type f{os_name} not supported and triple can't be determined, open issue to dSHARK team please :)"
         raise Exception(error_message)

shark/iree_utils/gpu_utils.py

@@ -18,6 +18,7 @@ import iree.runtime as ireert
 import ctypes
 from shark.parser import shark_args
 
+
 # Get the default gpu args given the architecture.
 def get_iree_gpu_args():
     ireert.flags.FUNCTION_INPUT_VALIDATION = False
@@ -39,8 +40,17 @@ def get_iree_gpu_args():
 # Get the default gpu args given the architecture.
 def get_iree_rocm_args():
     ireert.flags.FUNCTION_INPUT_VALIDATION = False
-    # TODO: find a way to get arch from code.
-    rocm_arch = "gfx908"
+    # get arch from rocminfo.
+    import re
+    import subprocess
+
+    rocm_arch = re.match(
+        r".*(gfx\w+)",
+        subprocess.check_output(
+            "rocminfo | grep -i 'gfx'", shell=True, text=True
+        ),
+    ).group(1)
+    print(f"Found rocm arch {rocm_arch}...")
     return [
         f"--iree-rocm-target-chip={rocm_arch}",
         "--iree-rocm-link-bc=true",

shark/iree_utils/vulkan_target_env_utils.py

@@ -0,0 +1,462 @@
+# Copyright 2020 The Nod Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import OrderedDict
+
+
+def get_vulkan_target_env(vulkan_target_triple):
+    arch, product, os = vulkan_target_triple.split("=")[1].split("-")
+    triple = (arch, product, os)
+    # get version
+    version = get_version(triple=triple)
+    # TODO get revision
+    revision = 120
+
+    # extensions
+    extensions = get_extensions(triple)
+    # get vendor
+    vendor = get_vendor(triple)
+    # get device type
+    device_type = get_device_type(triple)
+    # get capabilities
+    capabilities = get_vulkan_target_capabilities(triple)
+    target_env = f"#vk.target_env<{version}, r({revision}), {extensions}, {vendor}:{device_type}, #vk.caps< {capabilities} >>"
+    return target_env
+
+
+def get_vulkan_target_env_flag(vulkan_target_triple):
+    target_env = get_vulkan_target_env(vulkan_target_triple)
+    target_env_flag = f"--iree-vulkan-target-env={target_env}"
+    return target_env_flag
+
+
+def get_version(triple):
+    arch, product, os = triple
+    if os in ["android30", "android31"]:
+        return "v1.1"
+    if product in ["android30", "android31"]:
+        return "v1.1"
+    if arch in ["unknown"]:
+        return "v1.1"
+    return "v1.3"
+
+
+def get_extensions(triple):
+    def make_ext_list(ext_list):
+        res = ""
+        for e in ext_list:
+            res += e + ", "
+        res = f"[{res[:-2]}]"
+        return res
+
+    arch, product, os = triple
+    if arch == "m1":
+        ext = [
+            "VK_KHR_16bit_storage",
+            "VK_KHR_8bit_storage",
+            "VK_KHR_shader_float16_int8",
+            "VK_KHR_storage_buffer_storage_class",
+            "VK_KHR_variable_pointers",
+        ]
+        return make_ext_list(ext_list=ext)
+
+    if arch == "valhall":
+        ext = [
+            "VK_KHR_16bit_storage",
+            "VK_KHR_8bit_storage",
+            "VK_KHR_shader_float16_int8",
+            "VK_KHR_spirv_1_4",
+            "VK_KHR_storage_buffer_storage_class",
+            "VK_KHR_variable_pointers",
+        ]
+        return make_ext_list(ext_list=ext)
+
+    if arch == "adreno":
+        ext = [
+            "VK_KHR_16bit_storage",
+            "VK_KHR_shader_float16_int8",
+            "VK_KHR_spirv_1_4",
+            "VK_KHR_storage_buffer_storage_class",
+            "VK_KHR_variable_pointers",
+        ]
+        if os == "android31":
+            ext.append("VK_KHR_8bit_storage")
+        return make_ext_list(ext_list=ext)
+
+    if get_vendor(triple) == "SwiftShader":
+        ext = ["VK_KHR_storage_buffer_storage_class"]
+        return make_ext_list(ext_list=ext)
+
+    if arch == "unknown":
+        ext = [
+            "VK_KHR_storage_buffer_storage_class",
+            "VK_KHR_variable_pointers",
+        ]
+        return make_ext_list(ext_list=ext)
+
+    ext = [
+        "VK_KHR_16bit_storage",
+        "VK_KHR_8bit_storage",
+        "VK_KHR_shader_float16_int8",
+        "VK_KHR_spirv_1_4",
+        "VK_KHR_storage_buffer_storage_class",
+        "VK_KHR_variable_pointers",
+        "VK_EXT_subgroup_size_control",
+    ]
+
+    if get_vendor(triple) == "NVIDIA" or arch == "rdna3":
+        ext.append("VK_NV_cooperative_matrix")
+
+    return make_ext_list(ext_list=ext)
+
+
+def get_vendor(triple):
+    arch, product, os = triple
+    if arch == "unknown":
+        return "Unknown"
+    if arch in ["rdna1", "rdna2", "rdna3", "rgcn3", "rgcn4", "rgcn5"]:
+        return "AMD"
+    if arch == "valhall":
+        return "ARM"
+    if arch == "m1":
+        return "Apple"
+    if arch in ["turing", "ampere"]:
+        return "NVIDIA"
+    if arch == "ardeno":
+        return "Qualcomm"
+    if arch == "cpu":
+        if product == "swiftshader":
+            return "SwiftShader"
+        return "Unknown"
+    print(f"Vendor for target triple - {triple} not found. Using unknown")
+    return "Unknown"
+
+
+def get_device_type(triple):
+    arch, product, _ = triple
+    if arch == "unknown":
+        return "Unknown"
+    if arch == "cpu":
+        return "CPU"
+    if arch in ["turing", "ampere"]:
+        return "DiscreteGPU"
+    if arch in ["rdna1", "rdna2", "rdna3", "rgcn3", "rgcn5"]:
+        if product == "ivega10":
+            return "IntegratedGPU"
+        return "DiscreteGPU"
+    if arch in ["m1", "valhall", "adreno"]:
+        return "IntegratedGPU"
+    print(f"Device type for target triple - {triple} not found. Using unknown")
+    return "Unknown"
+
+
+# get all the capabilities for the device
+# TODO: make a dataclass for capabilites and init using vulkaninfo
+def get_vulkan_target_capabilities(triple):
+    def get_subgroup_val(l):
+        return int(sum([subgroup_feature[sgf] for sgf in l]))
+
+    cap = OrderedDict()
+    arch, product, os = triple
+    subgroup_feature = {
+        "Basic": 1,
+        "Vote": 2,
+        "Arithmetic": 4,
+        "Ballot": 8,
+        "Shuffle": 16,
+        "ShuffleRelative": 32,
+        "Clustered": 64,
+        "Quad": 128,
+        "PartitionedNV": 256,
+    }
+    cap["maxComputeSharedMemorySize"] = 16384
+    cap["maxComputeWorkGroupInvocations"] = 128
+    cap["maxComputeWorkGroupSize"] = [128, 128, 64]
+    cap["subgroupSize"] = 32
+    cap["subgroupFeatures"] = ["Basic"]
+    cap["minSubgroupSize"] = None
+    cap["maxSubgroupSize"] = None
+    cap["shaderFloat16"] = False
+    cap["shaderFloat64"] = False
+    cap["shaderInt8"] = False
+    cap["shaderInt16"] = False
+    cap["shaderInt64"] = False
+    cap["storageBuffer16BitAccess"] = False
+    cap["storagePushConstant16"] = False
+    cap["uniformAndStorageBuffer16BitAccess"] = False
+    cap["storageBuffer8BitAccess"] = False
+    cap["storagePushConstant8"] = False
+    cap["uniformAndStorageBuffer8BitAccess"] = False
+    cap["variablePointers"] = False
+    cap["variablePointersStorageBuffer"] = False
+    cap["coopmatCases"] = None
+
+    if arch in ["rdna1", "rdna2", "rdna3"]:
+        cap["maxComputeSharedMemorySize"] = 65536
+        cap["maxComputeWorkGroupInvocations"] = 1024
+        cap["maxComputeWorkGroupSize"] = [1024, 1024, 1024]
+
+        cap["subgroupSize"] = 64
+        cap["minSubgroupSize"] = 32
+        cap["maxSubgroupSize"] = 64
+        cap["subgroupFeatures"] = [
+            "Basic",
+            "Vote",
+            "Arithmetic",
+            "Ballot",
+            "Shuffle",
+            "ShuffleRelative",
+            "Clustered",
+            "Quad",
+        ]
+
+        cap["shaderFloat16"] = True
+        cap["shaderFloat64"] = True
+        cap["shaderInt8"] = True
+        cap["shaderInt16"] = True
+        cap["shaderInt64"] = True
+        cap["storageBuffer16BitAccess"] = True
+        cap["storagePushConstant16"] = True
+        cap["uniformAndStorageBuffer16BitAccess"] = True
+        cap["storageBuffer8BitAccess"] = True
+        cap["storagePushConstant8"] = True
+        cap["uniformAndStorageBuffer8BitAccess"] = True
+        cap["variablePointers"] = True
+        cap["variablePointersStorageBuffer"] = True
+
+        if arch == "rdna3":
+            # TODO: Get scope value
+            cap["coopmatCases"] = [
+                "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f16, resultType = f16, scope = #vk.scope<Subgroup>"
+            ]
+        if product == "rx5700xt":
+            cap["storagePushConstant16"] = False
+            cap["storagePushConstant8"] = False
+
+    elif arch in ["rgcn5", "rgcn4", "rgcn3"]:
+        cap["maxComputeSharedMemorySize"] = 65536
+        cap["maxComputeWorkGroupInvocations"] = 1024
+        cap["maxComputeWorkGroupSize"] = [1024, 1024, 1024]
+
+        cap["subgroupSize"] = 64
+        cap["subgroupFeatures"] = [
+            "Basic",
+            "Vote",
+            "Arithmetic",
+            "Ballot",
+            "Shuffle",
+            "ShuffleRelative",
+            "Clustered",
+            "Quad",
+        ]
+        cap["minSubgroupSize"] = 64
+        cap["maxSubgroupSize"] = 64
+
+        if arch == "rgcn5":
+            cap["shaderFloat16"] = True
+            cap["shaderFloat64"] = True
+
+            cap["storageBuffer16BitAccess"] = True
+
+        cap["shaderInt8"] = True
+        cap["shaderInt16"] = True
+        cap["shaderInt64"] = True
+
+        cap["storagePushConstant16"] = False
+        cap["uniformAndStorageBuffer16BitAccess"] = True
+        cap["storageBuffer8BitAccess"] = True
+        cap["storagePushConstant8"] = False
+        cap["uniformAndStorageBuffer8BitAccess"] = True
+
+        cap["variablePointers"] = True
+        cap["variablePointersStorageBuffer"] = True
+
+    elif arch == "m1":
+        cap["maxComputeSharedMemorySize"] = 32768
+        cap["maxComputeWorkGroupInvocations"] = 1024
+        cap["maxComputeWorkGroupSize"] = [1024, 1024, 1024]
+
+        cap["subgroupSize"] = 32
+        cap["subgroupFeatures"] = [
+            "Basic",
+            "Vote",
+            "Arithmetic",
+            "Ballot",
+            "Shuffle",
+            "ShuffleRelative",
+            "Quad",
+        ]
+
+        cap["shaderFloat16"] = True
+        cap["shaderFloat64"] = True
+        cap["shaderInt8"] = True
+        cap["shaderInt16"] = True
+        cap["shaderInt64"] = True
+        cap["storageBuffer16BitAccess"] = True
+        cap["storagePushConstant16"] = True
+        cap["uniformAndStorageBuffer16BitAccess"] = True
+        cap["storageBuffer8BitAccess"] = True
+        cap["storagePushConstant8"] = True
+        cap["uniformAndStorageBuffer8BitAccess"] = True
+        cap["variablePointers"] = True
+        cap["variablePointersStorageBuffer"] = True
+
+    elif arch == "valhall":
+        cap["maxComputeSharedMemorySize"] = 32768
+        cap["maxComputeWorkGroupInvocations"] = 512
+        cap["maxComputeWorkGroupSize"] = [512, 512, 512]
+
+        cap["subgroupSize"] = 16
+        cap["subgroupFeatures"] = [
+            "Basic",
+            "Vote",
+            "Arithmetic",
+            "Ballot",
+            "Clustered",
+            "Quad",
+        ]
+
+        if os == "android31":
+            cap["subgroupFeatures"].append("Shuffle")
+            cap["subgroupFeatures"].append("ShuffleRelative")
+
+        cap["shaderFloat16"] = True
+        cap["shaderInt8"] = True
+        cap["shaderInt16"] = True
+        cap["storageBuffer16BitAccess"] = True
+        cap["storagePushConstant16"] = True
+        cap["uniformAndStorageBuffer16BitAccess"] = True
+        cap["storageBuffer8BitAccess"] = True
+        cap["storagePushConstant8"] = True
+        cap["uniformAndStorageBuffer8BitAccess"] = True
+        cap["variablePointers"] = True
+        cap["variablePointersStorageBuffer"] = True
+
+    elif arch == "cpu":
+        if product == "swiftshader":
+            cap["maxComputeSharedMemorySize"] = 16384
+            cap["subgroupSize"] = 4
+            cap["subgroupFeatures"] = [
+                "Basic",
+                "Vote",
+                "Arithmetic",
+                "Ballot",
+                "Shuffle",
+                "ShuffleRelative",
+            ]
+
+    elif arch in ["ampere", "turing"]:
+        cap["maxComputeSharedMemorySize"] = 49152
+        cap["maxComputeWorkGroupInvocations"] = 1024
+        cap["maxComputeWorkGroupSize"] = [1024, 1024, 1024]
+
+        cap["subgroupSize"] = 32
+        cap["minSubgroupSize"] = 32
+        cap["maxSubgroupSize"] = 32
+        cap["subgroupFeatures"] = [
+            "Basic",
+            "Vote",
+            "Arithmetic",
+            "Ballot",
+            "Shuffle",
+            "ShuffleRelative",
+            "Clustered",
+            "Quad",
+        ]
+
+        cap["shaderFloat16"] = True
+        cap["shaderFloat64"] = True
+        cap["shaderInt8"] = True
+        cap["shaderInt16"] = True
+        cap["shaderInt64"] = True
+        cap["storageBuffer16BitAccess"] = True
+        cap["storagePushConstant16"] = True
+        cap["uniformAndStorageBuffer16BitAccess"] = True
+        cap["storageBuffer8BitAccess"] = True
+        cap["storagePushConstant8"] = True
+        cap["uniformAndStorageBuffer8BitAccess"] = True
+        cap["variablePointers"] = True
+        cap["variablePointersStorageBuffer"] = True
+
+        cap["coopmatCases"] = [
+            "mSize = 8, nSize = 8, kSize = 32, aType = i8, bType = i8, cType = i32, resultType = i32, scope = #vk.scope<Subgroup>",
+            "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f16, resultType = f16, scope = #vk.scope<Subgroup>",
+            "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f32, resultType = f32, scope = #vk.scope<Subgroup>",
+        ]
+
+    elif arch == "adreno":
+        cap["maxComputeSharedMemorySize"] = 32768
+        cap["maxComputeWorkGroupInvocations"] = 1024
+        cap["maxComputeWorkGroupSize"] = [1024, 1024, 64]
+
+        cap["subgroupSize"] = 64
+        cap["subgroupFeatures"] = [
+            "Basic",
+            "Vote",
+            "Arithmetic",
+            "Ballot",
+            "Shuffle",
+            "ShuffleRelative",
+            "Quad",
+        ]
+
+        cap["shaderFloat16"] = True
+        cap["shaderInt8"] = True
+        cap["shaderInt16"] = True
+
+        cap["storageBuffer16BitAccess"] = True
+        if os == "andorid31":
+            cap["uniformAndStorageBuffer8BitAccess"] = True
+
+        cap["variablePointers"] = True
+        cap["variablePointersStorageBuffer"] = True
+
+    elif arch == "unknown":
+        cap["subgroupSize"] = 64
+        cap["variablePointers"] = False
+        cap["variablePointersStorageBuffer"] = False
+    else:
+        print(
+            f"Architecture {arch} not matched. Using default vulkan target device capability"
+        )
+
+    def get_comma_sep_str(ele_list):
+        l = ""
+        for ele in ele_list:
+            l += f"{ele}, "
+        l = f"[{l[:-2]}]"
+        return l
+
+    res = ""
+    for k, v in cap.items():
+        if v is None or v == False:
+            continue
+        if isinstance(v, bool):
+            res += f"{k} = {'unit' if v == True else None}, "
+        elif isinstance(v, list):
+            if k == "subgroupFeatures":
+                res += f"subgroupFeatures = {get_subgroup_val(v)}: i32, "
+            elif k == "maxComputeWorkGroupSize":
+                res += f"maxComputeWorkGroupSize = dense<{get_comma_sep_str(v)}>: vector<{len(v)}xi32>, "
+            elif k == "coopmatCases":
+                cmc = ""
+                for case in v:
+                    cmc += f"#vk.coop_matrix_props<{case}>, "
+                res += f"cooperativeMatrixPropertiesNV = [{cmc[:-2]}], "
+            else:
+                res += f"{k} = {get_comma_sep_str(v)}, "
+        else:
+            res += f"{k} = {v}, "
+    res = res[:-2]
+    return res