fixed a bug where lm would crash with certain smaller models

.flake8

@@ -2,4 +2,4 @@
 count = 1
 show-source = 1
 select = E9,F63,F7,F82
-exclude = lit.cfg.py, apps/language_models/scripts/vicuna.py, apps/language_models/src/pipelines/minigpt4_pipeline.py, apps/language_models/langchain/h2oai_pipeline.py
+exclude = lit.cfg.py

.github/workflows/nightly.yml

@@ -50,13 +50,27 @@ jobs:
       shell: powershell
       run: |
         ./setup_venv.ps1
-        $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
-        pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SRT/pip-release-links.html
         python process_skipfiles.py
         pyinstaller .\apps\stable_diffusion\shark_sd.spec
-        mv ./dist/nodai_shark_studio.exe ./dist/nodai_shark_studio_${{ env.package_version_ }}.exe
-        signtool sign /f c:\g\shark_02152023.cer /fd certHash /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/nodai_shark_studio_${{ env.package_version_ }}.exe
-  
+        mv ./dist/shark_sd.exe ./dist/shark_sd_${{ env.package_version_ }}.exe
+        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_${{ env.package_version_ }}.exe
+        pyinstaller .\apps\stable_diffusion\shark_sd_cli.spec
+        python process_skipfiles.py
+        mv ./dist/shark_sd_cli.exe ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
+        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
+
+        
+    # GHA windows VM OOMs so disable for now
+    #- name: Build and validate the SHARK Runtime package
+    #  shell: powershell
+    #  run: |
+    #    $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
+    #    pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
+
+    #- uses: actions/upload-artifact@v2
+    #  with:
+    #    path: dist/*
+    
     - name: Upload Release Assets
       id: upload-release-assets
       uses: dwenegar/upload-release-assets@v1
@@ -64,7 +78,7 @@ jobs:
         GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
       with:
         release_id: ${{ steps.create_release.outputs.id }}
-        assets_path: ./dist/nodai*
+        assets_path: ./dist/*
         #asset_content_type: application/vnd.microsoft.portable-executable 
 
     - name: Publish Release
@@ -104,7 +118,7 @@ jobs:
         echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest toml
-        if [ -f requirements.txt ]; then pip install -r requirements.txt -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SRT/pip-release-links.html; fi
+        if [ -f requirements.txt ]; then pip install -r requirements.txt -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html; fi
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -144,7 +158,7 @@ jobs:
         source shark.venv/bin/activate
         package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
         SHARK_PACKAGE_VERSION=${package_version} \
-        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SRT/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://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
         # Install the built wheel
         pip install ./wheelhouse/nodai*
         # Validate the Models

.github/workflows/test-models.yml

@@ -0,0 +1,161 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Validate Models on Shark Runtime
+
+on:
+  push:
+    branches: [ main ]
+    paths-ignore:
+      - '**.md'
+      - 'shark/examples/**'
+  pull_request:
+    branches: [ main ]
+    paths-ignore:
+      - '**.md'
+      - 'shark/examples/**'
+  workflow_dispatch:
+
+# Ensure that only a single job or workflow using the same
+# concurrency group will run at a time. This would cancel
+# any in-progress jobs in the same github workflow and github
+# ref (e.g. refs/heads/main or refs/pull/<pr_number>/merge).
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  build-validate:
+    strategy:
+      fail-fast: true
+      matrix:
+        os: [7950x, icelake, a100, MacStudio, ubuntu-latest]
+        suite: [cpu,cuda,vulkan]
+        python-version: ["3.11"]
+        include:
+          - os: ubuntu-latest
+            suite: lint
+        exclude:
+          - os: ubuntu-latest
+            suite: vulkan
+          - os: ubuntu-latest
+            suite: cuda
+          - os: ubuntu-latest
+            suite: cpu
+          - os: MacStudio
+            suite: cuda
+          - os: MacStudio
+            suite: cpu
+          - os: icelake
+            suite: vulkan
+          - os: icelake
+            suite: cuda
+          - os: a100
+            suite: cpu
+          - os: 7950x
+            suite: cpu
+          - os: 7950x
+            suite: cuda
+
+    runs-on: ${{ matrix.os }}
+
+    steps:
+    - uses: actions/checkout@v3
+      if: matrix.os != '7950x'
+    
+    - name: Set Environment Variables
+      if: matrix.os != '7950x'
+      run: |
+        echo "SHORT_SHA=`git rev-parse --short=4 HEAD`" >> $GITHUB_ENV
+        echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
+        
+    - name: Set up Python Version File ${{ matrix.python-version }}
+      if: matrix.os == 'a100' ||  matrix.os == 'ubuntu-latest' ||  matrix.os == 'icelake'
+      run: |
+        # See https://github.com/actions/setup-python/issues/433
+        echo ${{ matrix.python-version }} >> $GITHUB_WORKSPACE/.python-version
+    
+    - name: Set up Python ${{ matrix.python-version }}
+      if: matrix.os == 'a100' ||  matrix.os == 'ubuntu-latest' ||  matrix.os == 'icelake'
+      uses: actions/setup-python@v4
+      with:
+        python-version: '${{ matrix.python-version }}'
+        #cache: 'pip'
+        #cache-dependency-path: |
+        #  **/requirements-importer.txt
+        #  **/requirements.txt
+    
+    - uses: actions/checkout@v2
+      if: matrix.os == '7950x'
+          
+    - name: Install dependencies
+      if: matrix.suite == 'lint'
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest toml black
+        
+    - name: Lint with flake8
+      if: matrix.suite == 'lint'
+      run: |
+        # black format check
+        black --version
+        black --check .
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --isolated --count --exit-zero --max-complexity=10 --max-line-length=127 \
+          --statistics --exclude lit.cfg.py
+
+    - name: Validate Models on CPU
+      if: matrix.suite == 'cpu'
+      run: |
+        cd $GITHUB_WORKSPACE
+        PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
+        source shark.venv/bin/activate
+        pytest --forked --benchmark --ci --ci_sha=${SHORT_SHA} --update_tank --tank_url="gs://shark_tank/nightly/" -k cpu 
+        gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv
+        gsutil cp gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv gs://shark-public/builder/bench_results/latest/bench_results_cpu_latest.csv
+
+    - name: Validate Models on NVIDIA GPU
+      if: matrix.suite == 'cuda'
+      run: |
+        cd $GITHUB_WORKSPACE
+        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
+        source shark.venv/bin/activate
+        pytest --forked --benchmark --ci --ci_sha=${SHORT_SHA} --update_tank --tank_url="gs://shark_tank/nightly/" -k cuda
+        gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${DATE}/bench_results_cuda_${SHORT_SHA}.csv
+        gsutil cp gs://shark-public/builder/bench_results/${DATE}/bench_results_cuda_${SHORT_SHA}.csv gs://shark-public/builder/bench_results/latest/bench_results_cuda_latest.csv
+        # Disabled due to black image bug
+        # python build_tools/stable_diffusion_testing.py --device=cuda 
+
+    - name: Validate Vulkan Models (MacOS)
+      if: matrix.suite == 'vulkan' && matrix.os == 'MacStudio'
+      run: |
+        cd $GITHUB_WORKSPACE
+        PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
+        source shark.venv/bin/activate
+        export DYLD_LIBRARY_PATH=/usr/local/lib/
+        echo $PATH
+        pip list | grep -E "torch|iree"
+        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/Volumes/builder/anush/shark_cache" --tank_url="gs://shark_tank/nightly/" -k vulkan --update_tank
+
+    - name: Validate Vulkan Models (a100)
+      if: matrix.suite == 'vulkan' && matrix.os == 'a100'
+      run: |
+        cd $GITHUB_WORKSPACE
+        PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
+        source shark.venv/bin/activate
+        pytest --forked --benchmark --ci --ci_sha=${SHORT_SHA} --update_tank --tank_url="gs://shark_tank/nightly/" -k vulkan
+        python build_tools/stable_diffusion_testing.py --device=vulkan
+
+    - name: Validate Vulkan Models (Windows)
+      if: matrix.suite == 'vulkan' && matrix.os == '7950x'
+      run: |
+        ./setup_venv.ps1
+        pytest -k vulkan -s
+
+    - 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

.github/workflows/test-studio.yml

@@ -1,86 +0,0 @@
-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
-# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
-
-name: Validate Shark Studio
-
-on:
-  push:
-    branches: [ main ]
-    paths-ignore:
-      - '**.md'
-      - 'shark/examples/**'
-  pull_request:
-    branches: [ main ]
-    paths-ignore:
-      - '**.md'
-      - 'shark/examples/**'
-  workflow_dispatch:
-
-# Ensure that only a single job or workflow using the same
-# concurrency group will run at a time. This would cancel
-# any in-progress jobs in the same github workflow and github
-# ref (e.g. refs/heads/main or refs/pull/<pr_number>/merge).
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  build-validate:
-    strategy:
-      fail-fast: true
-      matrix:
-        os: [nodai-ubuntu-builder-large]
-        suite: [cpu] #,cuda,vulkan]
-        python-version: ["3.11"]
-        include:
-          - os: nodai-ubuntu-builder-large
-            suite: lint
-
-    runs-on: ${{ matrix.os }}
-
-    steps:
-    - uses: actions/checkout@v3
-    
-    - name: Set Environment Variables
-      run: |
-        echo "SHORT_SHA=`git rev-parse --short=4 HEAD`" >> $GITHUB_ENV
-        echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
-        
-    - name: Set up Python Version File ${{ matrix.python-version }}
-      run: |
-        echo ${{ matrix.python-version }} >> $GITHUB_WORKSPACE/.python-version
-    
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
-      with:
-        python-version: '${{ matrix.python-version }}'
-          
-    - name: Install dependencies
-      if: matrix.suite == 'lint'
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install flake8 pytest toml black
-        
-    - name: Lint with flake8
-      if: matrix.suite == 'lint'
-      run: |
-        # black format check
-        black --version
-        black --check apps/shark_studio 
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --isolated --count --exit-zero --max-complexity=10 --max-line-length=127 \
-          --statistics --exclude lit.cfg.py
-
-    - name: Validate Models on CPU
-      if: matrix.suite == 'cpu'
-      run: |
-        cd $GITHUB_WORKSPACE
-        python${{ matrix.python-version }} -m venv shark.venv
-        source shark.venv/bin/activate
-        pip install -r requirements.txt --no-cache-dir
-        pip install -e .
-        pip uninstall -y torch
-        pip install torch==2.1.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
-        python apps/shark_studio/tests/api_test.py

.gitignore

@@ -2,8 +2,6 @@
 __pycache__/
 *.py[cod]
 *$py.class
-*.mlir
-*.vmfb
 
 # C extensions
 *.so
@@ -159,7 +157,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
+#.idea/
 
 # vscode related
 .vscode
@@ -182,23 +180,10 @@ generated_imgs/
 
 # Custom model related artefacts
 variants.json
-/models/
+models/
 
 # models folder
 apps/stable_diffusion/web/models/
 
 # Stencil annotators.
 stencil_annotator/
-
-# For DocuChat
-apps/language_models/langchain/user_path/
-db_dir_UserData
-
-# Embeded browser cache and other
-apps/stable_diffusion/web/EBWebView/
-
-# Llama2 tokenizer configs
-llama2_tokenizer_configs/
-
-# Webview2 runtime artefacts
-EBWebView/

.gitmodules

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

README.md

@@ -10,7 +10,7 @@ High Performance Machine Learning Distribution
   <summary>Prerequisites - Drivers </summary>
   
 #### Install your Windows hardware drivers
-* [AMD RDNA Users] Download the latest driver (23.2.1 is the oldest supported) [here](https://www.amd.com/en/support).
+* [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)
   
@@ -114,12 +114,12 @@ source shark.venv/bin/activate
 
 #### Windows 10/11 Users
 ```powershell
-(shark.venv) PS C:\g\shark> python .\apps\stable_diffusion\scripts\main.py --app="txt2img" --precision="fp16" --prompt="tajmahal, snow, sunflowers, oil on canvas" --device="vulkan"
+(shark.venv) PS C:\g\shark> python .\apps\stable_diffusion\scripts\txt2img.py --precision="fp16" --prompt="tajmahal, snow, sunflowers, oil on canvas" --device="vulkan"
 ```
 
 #### Linux / macOS Users
 ```shell
-python3.11 apps/stable_diffusion/scripts/main.py --app=txt2img --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
@@ -170,7 +170,7 @@ python -m pip install --upgrade pip
 This step pip installs SHARK and related packages on Linux Python 3.8, 3.10 and 3.11 and macOS / Windows Python 3.11
 
 ```shell
-pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f  https://nod-ai.github.io/SRT/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f  https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
 ```
 
 ### Run shark tank model tests.
@@ -254,6 +254,7 @@ if you want to instead incorporate this into a python script, you can pass the `
 ```
 shark_module = SharkInference(
         mlir_model,
+        func_name,
         device=args.device,
         mlir_dialect="tm_tensor",
         dispatch_benchmarks="all",
@@ -296,7 +297,7 @@ torch_mlir, func_name = mlir_importer.import_mlir(tracing_required=True)
 # SharkInference accepts mlir in linalg, mhlo, and tosa dialect.
 
 from shark.shark_inference import SharkInference
-shark_module = SharkInference(torch_mlir, device="cpu", mlir_dialect="linalg")
+shark_module = SharkInference(torch_mlir, func_name, device="cpu", mlir_dialect="linalg")
 shark_module.compile()
 result = shark_module.forward((input))
 
@@ -319,17 +320,12 @@ mhlo_ir = r"""builtin.module  {
 
 arg0 = np.ones((1, 4)).astype(np.float32)
 arg1 = np.ones((4, 1)).astype(np.float32)
-shark_module = SharkInference(mhlo_ir, device="cpu", mlir_dialect="mhlo")
+shark_module = SharkInference(mhlo_ir, func_name="forward", device="cpu", mlir_dialect="mhlo")
 shark_module.compile()
 result = shark_module.forward((arg0, arg1))
 ```
 </details>
 
-## Examples Using the REST API
-
-* [Setting up SHARK for use with Blender](./docs/shark_sd_blender.md)
-* [Setting up SHARK for use with Koboldcpp](./docs/shark_sd_koboldcpp.md)
-
 ## Supported and Validated Models
 
 SHARK is maintained to support the latest innovations in ML Models: 

apps/shark_studio/api/llm.py

@@ -1,179 +0,0 @@
-from turbine_models.custom_models import stateless_llama
-import time
-from shark.iree_utils.compile_utils import (
-    get_iree_compiled_module,
-    load_vmfb_using_mmap,
-)
-from apps.shark_studio.api.utils import get_resource_path
-import iree.runtime as ireert
-from itertools import chain
-import gc
-import os
-import torch
-from transformers import AutoTokenizer
-
-llm_model_map = {
-    "llama2_7b": {
-        "initializer": stateless_llama.export_transformer_model,
-        "hf_model_name": "meta-llama/Llama-2-7b-chat-hf",
-        "stop_token": 2,
-        "max_tokens": 4096,
-        "system_prompt": """<s>[INST] <<SYS>>Be concise. You are a helpful, respectful and honest assistant. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>>""",
-    },
-    "Trelis/Llama-2-7b-chat-hf-function-calling-v2": {
-        "initializer": stateless_llama.export_transformer_model,
-        "hf_model_name": "Trelis/Llama-2-7b-chat-hf-function-calling-v2",
-        "stop_token": 2,
-        "max_tokens": 4096,
-        "system_prompt": """<s>[INST] <<SYS>>Be concise. You are a helpful, respectful and honest assistant. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>>""",
-    },
-}
-
-
-class LanguageModel:
-    def __init__(
-        self,
-        model_name,
-        hf_auth_token=None,
-        device=None,
-        precision="fp32",
-        external_weights=None,
-        use_system_prompt=True,
-    ):
-        print(llm_model_map[model_name])
-        self.hf_model_name = llm_model_map[model_name]["hf_model_name"]
-        self.tempfile_name = get_resource_path("llm.torch.tempfile")
-        self.vmfb_name = get_resource_path("llm.vmfb.tempfile")
-        self.device = device
-        self.precision = precision
-        self.safe_name = self.hf_model_name.strip("/").replace("/", "_")
-        self.max_tokens = llm_model_map[model_name]["max_tokens"]
-        self.iree_module_dict = None
-        self.external_weight_file = None
-        if external_weights is not None:
-            self.external_weight_file = get_resource_path(
-                self.safe_name + "." + external_weights
-            )
-        self.use_system_prompt = use_system_prompt
-        self.global_iter = 0
-        if os.path.exists(self.vmfb_name) and (
-            external_weights is None or os.path.exists(str(self.external_weight_file))
-        ):
-            self.iree_module_dict = dict()
-            (
-                self.iree_module_dict["vmfb"],
-                self.iree_module_dict["config"],
-                self.iree_module_dict["temp_file_to_unlink"],
-            ) = load_vmfb_using_mmap(
-                self.vmfb_name,
-                device,
-                device_idx=0,
-                rt_flags=[],
-                external_weight_file=self.external_weight_file,
-            )
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                self.hf_model_name,
-                use_fast=False,
-                use_auth_token=hf_auth_token,
-            )
-        elif not os.path.exists(self.tempfile_name):
-            self.torch_ir, self.tokenizer = llm_model_map[model_name]["initializer"](
-                self.hf_model_name,
-                hf_auth_token,
-                compile_to="torch",
-                external_weights=external_weights,
-                external_weight_file=self.external_weight_file,
-            )
-            with open(self.tempfile_name, "w+") as f:
-                f.write(self.torch_ir)
-            del self.torch_ir
-            gc.collect()
-            self.compile()
-        else:
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                self.hf_model_name,
-                use_fast=False,
-                use_auth_token=hf_auth_token,
-            )
-            self.compile()
-
-    def compile(self) -> None:
-        # this comes with keys: "vmfb", "config", and "temp_file_to_unlink".
-        self.iree_module_dict = get_iree_compiled_module(
-            self.tempfile_name,
-            device=self.device,
-            mmap=True,
-            frontend="torch",
-            external_weight_file=self.external_weight_file,
-            write_to=self.vmfb_name,
-            extra_args=["--iree-global-opt-enable-quantized-matmul-reassociation"],
-        )
-        # TODO: delete the temp file
-
-    def sanitize_prompt(self, prompt):
-        print(prompt)
-        if isinstance(prompt, list):
-            prompt = list(chain.from_iterable(prompt))
-            prompt = " ".join([x for x in prompt if isinstance(x, str)])
-        prompt = prompt.replace("\n", " ")
-        prompt = prompt.replace("\t", " ")
-        prompt = prompt.replace("\r", " ")
-        if self.use_system_prompt and self.global_iter == 0:
-            prompt = llm_model_map["llama2_7b"]["system_prompt"] + prompt
-        prompt += " [/INST]"
-        print(prompt)
-        return prompt
-
-    def chat(self, prompt):
-        prompt = self.sanitize_prompt(prompt)
-
-        input_tensor = self.tokenizer(prompt, return_tensors="pt").input_ids
-
-        def format_out(results):
-            return torch.tensor(results.to_host()[0][0])
-
-        history = []
-        for iter in range(self.max_tokens):
-            st_time = time.time()
-            if iter == 0:
-                device_inputs = [
-                    ireert.asdevicearray(
-                        self.iree_module_dict["config"].device, input_tensor
-                    )
-                ]
-                token = self.iree_module_dict["vmfb"]["run_initialize"](*device_inputs)
-            else:
-                device_inputs = [
-                    ireert.asdevicearray(
-                        self.iree_module_dict["config"].device,
-                        token,
-                    )
-                ]
-                token = self.iree_module_dict["vmfb"]["run_forward"](*device_inputs)
-
-            total_time = time.time() - st_time
-            history.append(format_out(token))
-            yield self.tokenizer.decode(history), total_time
-
-            if format_out(token) == llm_model_map["llama2_7b"]["stop_token"]:
-                break
-
-        for i in range(len(history)):
-            if type(history[i]) != int:
-                history[i] = int(history[i])
-        result_output = self.tokenizer.decode(history)
-        self.global_iter += 1
-        return result_output, total_time
-
-
-if __name__ == "__main__":
-    lm = LanguageModel(
-        "Trelis/Llama-2-7b-chat-hf-function-calling-v2",
-        hf_auth_token=None,
-        device="cpu-task",
-        external_weights="safetensors",
-    )
-
-    print("model loaded")
-    for i in lm.chat("hi, what are you?"):
-        print(i)

apps/shark_studio/api/utils.py

@@ -1,12 +0,0 @@
-import os
-import sys
-
-
-def get_available_devices():
-    return ["cpu-task"]
-
-
-def get_resource_path(relative_path):
-    """Get absolute path to resource, works for dev and for PyInstaller"""
-    base_path = getattr(sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__)))
-    return os.path.join(base_path, relative_path)

apps/shark_studio/tests/api_test.py

@@ -1,34 +0,0 @@
-# Copyright 2023 Nod Labs, Inc
-#
-# Licensed under the Apache License v2.0 with LLVM Exceptions.
-# See https://llvm.org/LICENSE.txt for license information.
-# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-
-import logging
-import unittest
-from apps.shark_studio.api.llm import LanguageModel
-
-
-class LLMAPITest(unittest.TestCase):
-    def testLLMSimple(self):
-        lm = LanguageModel(
-            "Trelis/Llama-2-7b-chat-hf-function-calling-v2",
-            hf_auth_token=None,
-            device="cpu-task",
-            external_weights="safetensors",
-        )
-        count = 0
-        for msg, _ in lm.chat("hi, what are you?"):
-            # skip first token output
-            if count == 0:
-                count += 1
-                continue
-            assert (
-                msg.strip(" ") == "Hello"
-            ), f"LLM API failed to return correct response, expected 'Hello', received {msg}"
-            break
-
-
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.DEBUG)
-    unittest.main()

apps/shark_studio/web/index.py

@@ -1,426 +0,0 @@
-from multiprocessing import Process, freeze_support
-import os
-import sys
-import logging
-from ui.chat import chat_element
-
-if sys.platform == "darwin":
-    os.environ["DYLD_LIBRARY_PATH"] = "/usr/local/lib"
-    # import before IREE to avoid MLIR library issues
-    import torch_mlir
-
-# import PIL, transformers, sentencepiece  # ensures inclusion in pysintaller exe generation
-# from apps.stable_diffusion.src import args, clear_all
-# import apps.stable_diffusion.web.utils.global_obj as global_obj
-
-
-def launch_app(address):
-    from tkinter import Tk
-    import webview
-
-    window = Tk()
-
-    # get screen width and height of display and make it more reasonably
-    # sized as we aren't making it full-screen or maximized
-    width = int(window.winfo_screenwidth() * 0.81)
-    height = int(window.winfo_screenheight() * 0.91)
-    webview.create_window(
-        "SHARK AI Studio",
-        url=address,
-        width=width,
-        height=height,
-        text_select=True,
-    )
-    webview.start(private_mode=False, storage_path=os.getcwd())
-
-
-if __name__ == "__main__":
-    # if args.debug:
-    logging.basicConfig(level=logging.DEBUG)
-    # required to do multiprocessing in a pyinstaller freeze
-    freeze_support()
-    #    if args.api or "api" in args.ui.split(","):
-    #        from apps.stable_diffusion.web.ui import (
-    #            txt2img_api,
-    #            img2img_api,
-    #            upscaler_api,
-    #            inpaint_api,
-    #            outpaint_api,
-    #            llm_chat_api,
-    #        )
-    #
-    #        from fastapi import FastAPI, APIRouter
-    #        import uvicorn
-    #
-    #        # init global sd pipeline and config
-    #        global_obj._init()
-    #
-    #        app = FastAPI()
-    #        app.add_api_route("/sdapi/v1/txt2img", txt2img_api, methods=["post"])
-    #        app.add_api_route("/sdapi/v1/img2img", img2img_api, methods=["post"])
-    #        app.add_api_route("/sdapi/v1/inpaint", inpaint_api, methods=["post"])
-    #        app.add_api_route("/sdapi/v1/outpaint", outpaint_api, methods=["post"])
-    #        app.add_api_route("/sdapi/v1/upscaler", upscaler_api, methods=["post"])
-    #
-    #        # chat APIs needed for compatibility with multiple extensions using OpenAI API
-    #        app.add_api_route(
-    #            "/v1/chat/completions", llm_chat_api, methods=["post"]
-    #        )
-    #        app.add_api_route("/v1/completions", llm_chat_api, methods=["post"])
-    #        app.add_api_route("/chat/completions", llm_chat_api, methods=["post"])
-    #        app.add_api_route("/completions", llm_chat_api, methods=["post"])
-    #        app.add_api_route(
-    #            "/v1/engines/codegen/completions", llm_chat_api, methods=["post"]
-    #        )
-    #        app.include_router(APIRouter())
-    #        uvicorn.run(app, host="0.0.0.0", port=args.server_port)
-    #        sys.exit(0)
-    #
-    # Setup to use shark_tmp for gradio's temporary image files and clear any
-    # existing temporary images there if they exist. Then we can import gradio.
-    # It has to be in this order or gradio ignores what we've set up.
-    # from apps.stable_diffusion.web.utils.gradio_configs import (
-    #    config_gradio_tmp_imgs_folder,
-    # )
-
-    # config_gradio_tmp_imgs_folder()
-    import gradio as gr
-
-    # Create custom models folders if they don't exist
-    # from apps.stable_diffusion.web.ui.utils import create_custom_models_folders
-
-    # create_custom_models_folders()
-
-    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,
-    # txt2img_custom_model,
-    # txt2img_gallery,
-    # txt2img_png_info_img,
-    # txt2img_status,
-    # txt2img_sendto_img2img,
-    # txt2img_sendto_inpaint,
-    # txt2img_sendto_outpaint,
-    # txt2img_sendto_upscaler,
-    ## h2ogpt_upload,
-    ## h2ogpt_web,
-    # img2img_web,
-    # img2img_custom_model,
-    # img2img_gallery,
-    # img2img_init_image,
-    # img2img_status,
-    # img2img_sendto_inpaint,
-    # img2img_sendto_outpaint,
-    # img2img_sendto_upscaler,
-    # inpaint_web,
-    # inpaint_custom_model,
-    # inpaint_gallery,
-    # inpaint_init_image,
-    # inpaint_status,
-    # inpaint_sendto_img2img,
-    # inpaint_sendto_outpaint,
-    # inpaint_sendto_upscaler,
-    # outpaint_web,
-    # outpaint_custom_model,
-    # outpaint_gallery,
-    # outpaint_init_image,
-    # outpaint_status,
-    # outpaint_sendto_img2img,
-    # outpaint_sendto_inpaint,
-    # outpaint_sendto_upscaler,
-    # upscaler_web,
-    # upscaler_custom_model,
-    # upscaler_gallery,
-    # upscaler_init_image,
-    # upscaler_status,
-    # upscaler_sendto_img2img,
-    # upscaler_sendto_inpaint,
-    # upscaler_sendto_outpaint,
-    ##  lora_train_web,
-    ##  model_web,
-    ##  model_config_web,
-    # hf_models,
-    # modelmanager_sendto_txt2img,
-    # modelmanager_sendto_img2img,
-    # modelmanager_sendto_inpaint,
-    # modelmanager_sendto_outpaint,
-    # modelmanager_sendto_upscaler,
-    # stablelm_chat,
-    # minigpt4_web,
-    # outputgallery_web,
-    # outputgallery_tab_select,
-    # outputgallery_watch,
-    # outputgallery_filename,
-    # outputgallery_sendto_txt2img,
-    # outputgallery_sendto_img2img,
-    # outputgallery_sendto_inpaint,
-    # outputgallery_sendto_outpaint,
-    # outputgallery_sendto_upscaler,
-    # )
-
-    # init global sd pipeline and config
-    # global_obj._init()
-
-    def register_button_click(button, selectedid, inputs, outputs):
-        button.click(
-            lambda x: (
-                x[0]["name"] if len(x) != 0 else None,
-                gr.Tabs.update(selected=selectedid),
-            ),
-            inputs,
-            outputs,
-        )
-
-    def register_modelmanager_button(button, selectedid, inputs, outputs):
-        button.click(
-            lambda x: (
-                "None",
-                x,
-                gr.Tabs.update(selected=selectedid),
-            ),
-            inputs,
-            outputs,
-        )
-
-    def register_outputgallery_button(button, selectedid, inputs, outputs):
-        button.click(
-            lambda x: (
-                x,
-                gr.Tabs.update(selected=selectedid),
-            ),
-            inputs,
-            outputs,
-        )
-
-    with gr.Blocks(
-        css=dark_theme, analytics_enabled=False, title="Shark Studio 2.0 Beta"
-    ) as sd_web:
-        with gr.Tabs() as tabs:
-            # NOTE: If adding, removing, or re-ordering tabs, make sure that they
-            # have a unique id that doesn't clash with any of the other tabs,
-            # and that the order in the code here is the order they should
-            # appear in the ui, as the id value doesn't determine the order.
-
-            # Where possible, avoid changing the id of any tab that is the
-            # destination of one of the 'send to' buttons. If you do have to change
-            # that id, make sure you update the relevant register_button_click calls
-            # further down with the new id.
-            # with gr.TabItem(label="Text-to-Image", id=0):
-            #    txt2img_web.render()
-            # with gr.TabItem(label="Image-to-Image", id=1):
-            #    img2img_web.render()
-            # with gr.TabItem(label="Inpainting", id=2):
-            #    inpaint_web.render()
-            # with gr.TabItem(label="Outpainting", id=3):
-            #    outpaint_web.render()
-            # with gr.TabItem(label="Upscaler", id=4):
-            #    upscaler_web.render()
-            # if args.output_gallery:
-            #    with gr.TabItem(label="Output Gallery", id=5) as og_tab:
-            #        outputgallery_web.render()
-
-            #    # extra output gallery configuration
-            #    outputgallery_tab_select(og_tab.select)
-            #    outputgallery_watch(
-            #        [
-            #            txt2img_status,
-            #            img2img_status,
-            #            inpaint_status,
-            #            outpaint_status,
-            #            upscaler_status,
-            #        ]
-            #    )
-            ##  with gr.TabItem(label="Model Manager", id=6):
-            ##      model_web.render()
-            ##  with gr.TabItem(label="LoRA Training (Experimental)", id=7):
-            ##      lora_train_web.render()
-            with gr.TabItem(label="Chat Bot", id=0):
-                chat_element.render()
-            ##  with gr.TabItem(
-            ##      label="Generate Sharding Config (Experimental)", id=9
-            ##  ):
-            ##      model_config_web.render()
-            # with gr.TabItem(label="MultiModal (Experimental)", id=10):
-            #    minigpt4_web.render()
-            # with gr.TabItem(label="DocuChat Upload", id=11):
-            #     h2ogpt_upload.render()
-            # with gr.TabItem(label="DocuChat(Experimental)", id=12):
-            #     h2ogpt_web.render()
-
-        # send to buttons
-        # register_button_click(
-        #    txt2img_sendto_img2img,
-        #    1,
-        #    [txt2img_gallery],
-        #    [img2img_init_image, tabs],
-        # )
-        # register_button_click(
-        #    txt2img_sendto_inpaint,
-        #    2,
-        #    [txt2img_gallery],
-        #    [inpaint_init_image, tabs],
-        # )
-        # register_button_click(
-        #    txt2img_sendto_outpaint,
-        #    3,
-        #    [txt2img_gallery],
-        #    [outpaint_init_image, tabs],
-        # )
-        # register_button_click(
-        #    txt2img_sendto_upscaler,
-        #    4,
-        #    [txt2img_gallery],
-        #    [upscaler_init_image, tabs],
-        # )
-        # register_button_click(
-        #    img2img_sendto_inpaint,
-        #    2,
-        #    [img2img_gallery],
-        #    [inpaint_init_image, tabs],
-        # )
-        # register_button_click(
-        #    img2img_sendto_outpaint,
-        #    3,
-        #    [img2img_gallery],
-        #    [outpaint_init_image, tabs],
-        # )
-        # register_button_click(
-        #    img2img_sendto_upscaler,
-        #    4,
-        #    [img2img_gallery],
-        #    [upscaler_init_image, tabs],
-        # )
-        # register_button_click(
-        #    inpaint_sendto_img2img,
-        #    1,
-        #    [inpaint_gallery],
-        #    [img2img_init_image, tabs],
-        # )
-        # register_button_click(
-        #    inpaint_sendto_outpaint,
-        #    3,
-        #    [inpaint_gallery],
-        #    [outpaint_init_image, tabs],
-        # )
-        # register_button_click(
-        #    inpaint_sendto_upscaler,
-        #    4,
-        #    [inpaint_gallery],
-        #    [upscaler_init_image, tabs],
-        # )
-        # register_button_click(
-        #    outpaint_sendto_img2img,
-        #    1,
-        #    [outpaint_gallery],
-        #    [img2img_init_image, tabs],
-        # )
-        # register_button_click(
-        #    outpaint_sendto_inpaint,
-        #    2,
-        #    [outpaint_gallery],
-        #    [inpaint_init_image, tabs],
-        # )
-        # register_button_click(
-        #    outpaint_sendto_upscaler,
-        #    4,
-        #    [outpaint_gallery],
-        #    [upscaler_init_image, tabs],
-        # )
-        # register_button_click(
-        #    upscaler_sendto_img2img,
-        #    1,
-        #    [upscaler_gallery],
-        #    [img2img_init_image, tabs],
-        # )
-        # register_button_click(
-        #    upscaler_sendto_inpaint,
-        #    2,
-        #    [upscaler_gallery],
-        #    [inpaint_init_image, tabs],
-        # )
-        # register_button_click(
-        #    upscaler_sendto_outpaint,
-        #    3,
-        #    [upscaler_gallery],
-        #    [outpaint_init_image, tabs],
-        # )
-        # if args.output_gallery:
-        #    register_outputgallery_button(
-        #        outputgallery_sendto_txt2img,
-        #        0,
-        #        [outputgallery_filename],
-        #        [txt2img_png_info_img, tabs],
-        #    )
-        #    register_outputgallery_button(
-        #        outputgallery_sendto_img2img,
-        #        1,
-        #        [outputgallery_filename],
-        #        [img2img_init_image, tabs],
-        #    )
-        #    register_outputgallery_button(
-        #        outputgallery_sendto_inpaint,
-        #        2,
-        #        [outputgallery_filename],
-        #        [inpaint_init_image, tabs],
-        #    )
-        #    register_outputgallery_button(
-        #        outputgallery_sendto_outpaint,
-        #        3,
-        #        [outputgallery_filename],
-        #        [outpaint_init_image, tabs],
-        #    )
-        #    register_outputgallery_button(
-        #        outputgallery_sendto_upscaler,
-        #        4,
-        #        [outputgallery_filename],
-        #        [upscaler_init_image, tabs],
-        #    )
-        # register_modelmanager_button(
-        #    modelmanager_sendto_txt2img,
-        #    0,
-        #    [hf_models],
-        #    [txt2img_custom_model, tabs],
-        # )
-        # register_modelmanager_button(
-        #    modelmanager_sendto_img2img,
-        #    1,
-        #    [hf_models],
-        #    [img2img_custom_model, tabs],
-        # )
-        # register_modelmanager_button(
-        #    modelmanager_sendto_inpaint,
-        #    2,
-        #    [hf_models],
-        #    [inpaint_custom_model, tabs],
-        # )
-        # register_modelmanager_button(
-        #    modelmanager_sendto_outpaint,
-        #    3,
-        #    [hf_models],
-        #    [outpaint_custom_model, tabs],
-        # )
-        # register_modelmanager_button(
-        #    modelmanager_sendto_upscaler,
-        #    4,
-        #    [hf_models],
-        #    [upscaler_custom_model, tabs],
-        # )
-
-    sd_web.queue()
-    # if args.ui == "app":
-    #    t = Process(
-    #        target=launch_app, args=[f"http://localhost:{args.server_port}"]
-    #    )
-    #    t.start()
-    sd_web.launch(
-        share=True,
-        inbrowser=True,
-        server_name="0.0.0.0",
-        server_port=11911,  # args.server_port,
-    )

apps/shark_studio/web/ui/chat.py

@@ -1,298 +0,0 @@
-import gradio as gr
-import time
-import os
-from pathlib import Path
-from datetime import datetime as dt
-import json
-import sys
-from apps.shark_studio.api.utils import (
-    get_available_devices,
-)
-from apps.shark_studio.api.llm import (
-    llm_model_map,
-    LanguageModel,
-)
-
-
-def user(message, history):
-    # Append the user's message to the conversation history
-    return "", history + [[message, ""]]
-
-
-language_model = None
-
-
-def create_prompt(model_name, history, prompt_prefix):
-    return ""
-
-
-def get_default_config():
-    return False
-
-
-# model_vmfb_key = ""
-
-
-def chat_fn(
-    prompt_prefix,
-    history,
-    model,
-    device,
-    precision,
-    download_vmfb,
-    config_file,
-    cli=False,
-):
-    global language_model
-    if language_model is None:
-        history[-1][-1] = "Getting the model ready..."
-        yield history, ""
-        language_model = LanguageModel(
-            model,
-            device=device,
-            precision=precision,
-            external_weights="safetensors",
-            external_weight_file="llama2_7b.safetensors",
-            use_system_prompt=prompt_prefix,
-        )
-        history[-1][-1] = "Getting the model ready... Done"
-        yield history, ""
-        history[-1][-1] = ""
-    token_count = 0
-    total_time = 0.001  # In order to avoid divide by zero error
-    prefill_time = 0
-    is_first = True
-    for text, exec_time in language_model.chat(history):
-        history[-1][-1] = text
-        if is_first:
-            prefill_time = exec_time
-            is_first = False
-            yield history, f"Prefill: {prefill_time:.2f}"
-        else:
-            total_time += exec_time
-            token_count += 1
-            tokens_per_sec = token_count / total_time
-            yield history, f"Prefill: {prefill_time:.2f} seconds\n Decode: {tokens_per_sec:.2f} tokens/sec"
-
-
-def llm_chat_api(InputData: dict):
-    return None
-    print(f"Input keys : {InputData.keys()}")
-    # print(f"model : {InputData['model']}")
-    is_chat_completion_api = (
-        "messages" in InputData.keys()
-    )  # else it is the legacy `completion` api
-    # For Debugging input data from API
-    # if is_chat_completion_api:
-    #     print(f"message -> role : {InputData['messages'][0]['role']}")
-    #     print(f"message -> content : {InputData['messages'][0]['content']}")
-    # else:
-    #     print(f"prompt : {InputData['prompt']}")
-    # print(f"max_tokens : {InputData['max_tokens']}") # Default to 128 for now
-    global vicuna_model
-    model_name = InputData["model"] if "model" in InputData.keys() else "codegen"
-    model_path = llm_model_map[model_name]
-    device = "cpu-task"
-    precision = "fp16"
-    max_toks = None if "max_tokens" not in InputData.keys() else InputData["max_tokens"]
-    if max_toks is None:
-        max_toks = 128 if model_name == "codegen" else 512
-
-    # make it working for codegen first
-    from apps.language_models.scripts.vicuna import (
-        UnshardedVicuna,
-    )
-
-    device_id = None
-    if vicuna_model == 0:
-        if "cuda" in device:
-            device = "cuda"
-        elif "sync" in device:
-            device = "cpu-sync"
-        elif "task" in device:
-            device = "cpu-task"
-        elif "vulkan" in device:
-            device_id = int(device.split("://")[1])
-            device = "vulkan"
-        else:
-            print("unrecognized device")
-
-        vicuna_model = UnshardedVicuna(
-            model_name,
-            hf_model_path=model_path,
-            device=device,
-            precision=precision,
-            max_num_tokens=max_toks,
-            download_vmfb=True,
-            load_mlir_from_shark_tank=True,
-            device_id=device_id,
-        )
-
-    # TODO: add role dict for different models
-    if is_chat_completion_api:
-        # TODO: add funtionality for multiple messages
-        prompt = create_prompt(model_name, [(InputData["messages"][0]["content"], "")])
-    else:
-        prompt = InputData["prompt"]
-    print("prompt = ", prompt)
-
-    res = vicuna_model.generate(prompt)
-    res_op = None
-    for op in res:
-        res_op = op
-
-    if is_chat_completion_api:
-        choices = [
-            {
-                "index": 0,
-                "message": {
-                    "role": "assistant",
-                    "content": res_op,  # since we are yeilding the result
-                },
-                "finish_reason": "stop",  # or length
-            }
-        ]
-    else:
-        choices = [
-            {
-                "text": res_op,
-                "index": 0,
-                "logprobs": None,
-                "finish_reason": "stop",  # or length
-            }
-        ]
-    end_time = dt.now().strftime("%Y%m%d%H%M%S%f")
-    return {
-        "id": end_time,
-        "object": "chat.completion" if is_chat_completion_api else "text_completion",
-        "created": int(end_time),
-        "choices": choices,
-    }
-
-
-def view_json_file(file_obj):
-    content = ""
-    with open(file_obj.name, "r") as fopen:
-        content = fopen.read()
-    return content
-
-
-with gr.Blocks(title="Chat") as chat_element:
-    with gr.Row():
-        model_choices = list(llm_model_map.keys())
-        model = gr.Dropdown(
-            label="Select Model",
-            value=model_choices[0],
-            choices=model_choices,
-            allow_custom_value=True,
-        )
-        supported_devices = get_available_devices()
-        enabled = True
-        if len(supported_devices) == 0:
-            supported_devices = ["cpu-task"]
-        supported_devices = [x for x in supported_devices if "sync" not in x]
-        device = gr.Dropdown(
-            label="Device",
-            value=supported_devices[0],
-            choices=supported_devices,
-            interactive=enabled,
-            allow_custom_value=True,
-        )
-        precision = gr.Radio(
-            label="Precision",
-            value="int4",
-            choices=[
-                # "int4",
-                # "int8",
-                # "fp16",
-                "fp32",
-            ],
-            visible=False,
-        )
-        tokens_time = gr.Textbox(label="Tokens generated per second")
-        with gr.Column():
-            download_vmfb = gr.Checkbox(
-                label="Download vmfb from Shark tank if available",
-                value=True,
-                interactive=True,
-            )
-            prompt_prefix = gr.Checkbox(
-                label="Add System Prompt",
-                value=False,
-                interactive=True,
-            )
-
-    chatbot = gr.Chatbot(height=500)
-    with gr.Row():
-        with gr.Column():
-            msg = gr.Textbox(
-                label="Chat Message Box",
-                placeholder="Chat Message Box",
-                show_label=False,
-                interactive=enabled,
-                container=False,
-            )
-        with gr.Column():
-            with gr.Row():
-                submit = gr.Button("Submit", interactive=enabled)
-                stop = gr.Button("Stop", interactive=enabled)
-                clear = gr.Button("Clear", interactive=enabled)
-
-    with gr.Row(visible=False):
-        with gr.Group():
-            config_file = gr.File(label="Upload sharding configuration", visible=False)
-            json_view_button = gr.Button(label="View as JSON", visible=False)
-        json_view = gr.JSON(interactive=True, visible=False)
-        json_view_button.click(
-            fn=view_json_file, inputs=[config_file], outputs=[json_view]
-        )
-    submit_event = msg.submit(
-        fn=user,
-        inputs=[msg, chatbot],
-        outputs=[msg, chatbot],
-        show_progress=False,
-        queue=False,
-    ).then(
-        fn=chat_fn,
-        inputs=[
-            prompt_prefix,
-            chatbot,
-            model,
-            device,
-            precision,
-            download_vmfb,
-            config_file,
-        ],
-        outputs=[chatbot, tokens_time],
-        show_progress=False,
-        queue=True,
-    )
-    submit_click_event = submit.click(
-        fn=user,
-        inputs=[msg, chatbot],
-        outputs=[msg, chatbot],
-        show_progress=False,
-        queue=False,
-    ).then(
-        fn=chat_fn,
-        inputs=[
-            prompt_prefix,
-            chatbot,
-            model,
-            device,
-            precision,
-            download_vmfb,
-            config_file,
-        ],
-        outputs=[chatbot, tokens_time],
-        show_progress=False,
-        queue=True,
-    )
-    stop.click(
-        fn=None,
-        inputs=None,
-        outputs=None,
-        cancels=[submit_event, submit_click_event],
-        queue=False,
-    )
-    clear.click(lambda: None, None, [chatbot], queue=False)

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 +1,6 @@
+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
+from apps.stable_diffusion.scripts.upscaler import upscaler_inf
+from apps.stable_diffusion.scripts.train_lora_word import lora_train

apps/stable_diffusion/scripts/img2img.py

@@ -0,0 +1,382 @@
+import sys
+import torch
+import time
+from PIL import Image
+from apps.stable_diffusion.src import (
+    args,
+    Image2ImagePipeline,
+    StencilPipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+from apps.stable_diffusion.src.utils import get_generation_text_info
+
+
+schedulers = None
+
+# set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
+init_iree_vulkan_target_triple = args.iree_vulkan_target_triple
+init_use_tuned = args.use_tuned
+init_import_mlir = args.import_mlir
+
+
+# For stencil, the input image can be of any size but we need to ensure that
+# it conforms with our model contraints :-
+#   Both width and height should be > 384 and multiple of 8.
+# This utility function performs the transformation on the input image while
+# also maintaining the aspect ratio before sending it to the stencil pipeline.
+def resize_stencil(image: Image.Image):
+    width, height = image.size
+    aspect_ratio = width / height
+    min_size = min(width, height)
+    if min_size < 384:
+        n_size = 384
+        if width == min_size:
+            width = n_size
+            height = n_size / aspect_ratio
+        else:
+            height = n_size
+            width = n_size * aspect_ratio
+    width = int(width)
+    height = int(height)
+    n_width = width // 8
+    n_height = height // 8
+    n_width *= 8
+    n_height *= 8
+    new_image = image.resize((n_width, n_height))
+    return new_image, n_width, n_height
+
+
+# Exposed to UI.
+def img2img_inf(
+    prompt: str,
+    negative_prompt: str,
+    init_image,
+    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,
+    use_stencil: str,
+    save_metadata_to_json: bool,
+    save_metadata_to_png: bool,
+    lora_weights: str,
+    lora_hf_id: str,
+):
+    from apps.stable_diffusion.web.ui.utils import (
+        get_custom_model_pathfile,
+        Config,
+    )
+    import apps.stable_diffusion.web.utils.global_obj as global_obj
+    from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+        SD_STATE_CANCEL,
+    )
+
+    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 = "not none"
+
+    if init_image is None:
+        return None, "An Initial Image is required"
+    image = init_image.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 = get_custom_model_pathfile(custom_model)
+    else:
+        args.hf_model_id = custom_model
+
+    use_lora = ""
+    if lora_weights == "None" and not lora_hf_id:
+        use_lora = ""
+    elif not lora_hf_id:
+        use_lora = lora_weights
+    else:
+        use_lora = lora_hf_id
+    args.use_lora = use_lora
+
+    args.save_metadata_to_json = save_metadata_to_json
+    args.write_metadata_to_png = save_metadata_to_png
+
+    use_stencil = None if use_stencil == "None" else use_stencil
+    args.use_stencil = use_stencil
+    if use_stencil is not None:
+        args.scheduler = "DDIM"
+        args.hf_model_id = "runwayml/stable-diffusion-v1-5"
+        image, width, height = resize_stencil(image)
+    elif 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."
+            )
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    args.precision = precision
+    dtype = torch.float32 if precision == "fp32" else torch.half
+    new_config_obj = Config(
+        "img2img",
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        height,
+        width,
+        device,
+        use_lora=use_lora,
+        use_stencil=use_stencil,
+    )
+    if (
+        not global_obj.get_sd_obj()
+        or global_obj.get_cfg_obj() != new_config_obj
+    ):
+        global_obj.clear_cache()
+        global_obj.set_cfg_obj(new_config_obj)
+        args.batch_count = batch_count
+        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 = init_iree_vulkan_target_triple
+        args.use_tuned = init_use_tuned
+        args.import_mlir = init_import_mlir
+        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]
+
+        if use_stencil is not None:
+            args.use_tuned = False
+            global_obj.set_sd_obj(
+                StencilPipeline.from_pretrained(
+                    scheduler_obj,
+                    args.import_mlir,
+                    args.hf_model_id,
+                    args.ckpt_loc,
+                    args.custom_vae,
+                    args.precision,
+                    args.max_length,
+                    args.batch_size,
+                    args.height,
+                    args.width,
+                    args.use_base_vae,
+                    args.use_tuned,
+                    low_cpu_mem_usage=args.low_cpu_mem_usage,
+                    use_stencil=use_stencil,
+                    debug=args.import_debug if args.import_mlir else False,
+                    use_lora=use_lora,
+                )
+            )
+        else:
+            global_obj.set_sd_obj(
+                Image2ImagePipeline.from_pretrained(
+                    scheduler_obj,
+                    args.import_mlir,
+                    args.hf_model_id,
+                    args.ckpt_loc,
+                    args.custom_vae,
+                    args.precision,
+                    args.max_length,
+                    args.batch_size,
+                    args.height,
+                    args.width,
+                    args.use_base_vae,
+                    args.use_tuned,
+                    low_cpu_mem_usage=args.low_cpu_mem_usage,
+                    debug=args.import_debug if args.import_mlir else False,
+                    use_lora=use_lora,
+                )
+            )
+
+    global_obj.set_schedulers(schedulers[scheduler])
+
+    start_time = time.time()
+    global_obj.get_sd_obj().log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+    extra_info = {"STRENGTH": strength}
+    text_output = ""
+    for current_batch in range(batch_count):
+        if current_batch > 0:
+            img_seed = utils.sanitize_seed(-1)
+        out_imgs = global_obj.get_sd_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,
+            use_stencil=use_stencil,
+        )
+        seeds.append(img_seed)
+        total_time = time.time() - start_time
+        text_output = get_generation_text_info(seeds, device)
+        text_output += "\n" + global_obj.get_sd_obj().log
+        text_output += f"\nTotal image(s) generation time: {total_time:.4f}sec"
+
+        if global_obj.get_sd_status() == SD_STATE_CANCEL:
+            break
+        else:
+            save_output_img(out_imgs[0], img_seed, extra_info)
+            generated_imgs.extend(out_imgs)
+            yield generated_imgs, text_output
+
+    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()
+
+    image = Image.open(args.img_path).convert("RGB")
+    # When the models get uploaded, it should be default to False.
+    args.import_mlir = True
+
+    use_stencil = args.use_stencil
+    if use_stencil:
+        args.scheduler = "DDIM"
+        args.hf_model_id = "runwayml/stable-diffusion-v1-5"
+        image, args.width, args.height = resize_stencil(image)
+    elif 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."
+            )
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    set_init_device_flags()
+    schedulers = get_schedulers(args.hf_model_id)
+    scheduler_obj = schedulers[args.scheduler]
+    seed = utils.sanitize_seed(args.seed)
+    # Adjust for height and width based on model
+
+    if use_stencil:
+        img2img_obj = StencilPipeline.from_pretrained(
+            scheduler_obj,
+            args.import_mlir,
+            args.hf_model_id,
+            args.ckpt_loc,
+            args.custom_vae,
+            args.precision,
+            args.max_length,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.use_base_vae,
+            args.use_tuned,
+            low_cpu_mem_usage=args.low_cpu_mem_usage,
+            use_stencil=use_stencil,
+            debug=args.import_debug if args.import_mlir else False,
+            use_lora=args.use_lora,
+        )
+    else:
+        img2img_obj = Image2ImagePipeline.from_pretrained(
+            scheduler_obj,
+            args.import_mlir,
+            args.hf_model_id,
+            args.ckpt_loc,
+            args.custom_vae,
+            args.precision,
+            args.max_length,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.use_base_vae,
+            args.use_tuned,
+            low_cpu_mem_usage=args.low_cpu_mem_usage,
+            debug=args.import_debug if args.import_mlir else False,
+            use_lora=args.use_lora,
+        )
+
+    start_time = time.time()
+    generated_imgs = img2img_obj.generate_images(
+        args.prompts,
+        args.negative_prompts,
+        image,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.steps,
+        args.strength,
+        args.guidance_scale,
+        seed,
+        args.max_length,
+        dtype,
+        args.use_base_vae,
+        cpu_scheduling,
+        use_stencil=use_stencil,
+    )
+    total_time = time.time() - start_time
+    text_output = f"prompt={args.prompts}"
+    text_output += f"\nnegative prompt={args.negative_prompts}"
+    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+    text_output += f"\nscheduler={args.scheduler}, device={args.device}"
+    text_output += f"\nsteps={args.steps}, strength={args.strength}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
+    text_output += (
+        f", batch size={args.batch_size}, max_length={args.max_length}"
+    )
+    text_output += img2img_obj.log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    extra_info = {"STRENGTH": args.strength}
+    save_output_img(generated_imgs[0], seed, extra_info)
+    print(text_output)

apps/stable_diffusion/scripts/inpaint.py

@@ -0,0 +1,287 @@
+import torch
+import time
+from PIL import Image
+from apps.stable_diffusion.src import (
+    args,
+    InpaintPipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+from apps.stable_diffusion.src.utils import get_generation_text_info
+
+
+schedulers = None
+
+# set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
+init_iree_vulkan_target_triple = args.iree_vulkan_target_triple
+init_use_tuned = args.use_tuned
+init_import_mlir = args.import_mlir
+
+
+# Exposed to UI.
+def inpaint_inf(
+    prompt: str,
+    negative_prompt: str,
+    image_dict,
+    height: int,
+    width: int,
+    inpaint_full_res: bool,
+    inpaint_full_res_padding: 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,
+    lora_weights: str,
+    lora_hf_id: str,
+):
+    from apps.stable_diffusion.web.ui.utils import (
+        get_custom_model_pathfile,
+        Config,
+    )
+    import apps.stable_diffusion.web.utils.global_obj as global_obj
+    from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+        SD_STATE_CANCEL,
+    )
+
+    global schedulers
+
+    args.prompts = [prompt]
+    args.negative_prompts = [negative_prompt]
+    args.guidance_scale = guidance_scale
+    args.steps = steps
+    args.scheduler = scheduler
+    args.img_path = "not none"
+    args.mask_path = "not none"
+
+    # 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 = get_custom_model_pathfile(custom_model)
+    else:
+        args.hf_model_id = custom_model
+
+    use_lora = ""
+    if lora_weights == "None" and not lora_hf_id:
+        use_lora = ""
+    elif not lora_hf_id:
+        use_lora = lora_weights
+    else:
+        use_lora = lora_hf_id
+    args.use_lora = use_lora
+
+    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(
+        "inpaint",
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        height,
+        width,
+        device,
+        use_lora=use_lora,
+        use_stencil=None,
+    )
+    if (
+        not global_obj.get_sd_obj()
+        or global_obj.get_cfg_obj() != new_config_obj
+    ):
+        global_obj.clear_cache()
+        global_obj.set_cfg_obj(new_config_obj)
+        args.precision = precision
+        args.batch_count = batch_count
+        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 = init_iree_vulkan_target_triple
+        args.use_tuned = init_use_tuned
+        args.import_mlir = init_import_mlir
+        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]
+        global_obj.set_sd_obj(
+            InpaintPipeline.from_pretrained(
+                scheduler=scheduler_obj,
+                import_mlir=args.import_mlir,
+                model_id=args.hf_model_id,
+                ckpt_loc=args.ckpt_loc,
+                precision=args.precision,
+                max_length=args.max_length,
+                batch_size=args.batch_size,
+                height=args.height,
+                width=args.width,
+                use_base_vae=args.use_base_vae,
+                use_tuned=args.use_tuned,
+                custom_vae=args.custom_vae,
+                low_cpu_mem_usage=args.low_cpu_mem_usage,
+                debug=args.import_debug if args.import_mlir else False,
+                use_lora=use_lora,
+            )
+        )
+
+    global_obj.set_schedulers(schedulers[scheduler])
+
+    start_time = time.time()
+    global_obj.get_sd_obj().log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+    image = image_dict["image"]
+    mask_image = image_dict["mask"]
+    text_output = ""
+    for i in range(batch_count):
+        if i > 0:
+            img_seed = utils.sanitize_seed(-1)
+        out_imgs = global_obj.get_sd_obj().generate_images(
+            prompt,
+            negative_prompt,
+            image,
+            mask_image,
+            batch_size,
+            height,
+            width,
+            inpaint_full_res,
+            inpaint_full_res_padding,
+            steps,
+            guidance_scale,
+            img_seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        seeds.append(img_seed)
+        total_time = time.time() - start_time
+        text_output = get_generation_text_info(seeds, device)
+        text_output += "\n" + global_obj.get_sd_obj().log
+        text_output += f"\nTotal image(s) generation time: {total_time:.4f}sec"
+
+        if global_obj.get_sd_status() == SD_STATE_CANCEL:
+            break
+        else:
+            save_output_img(out_imgs[0], img_seed)
+            generated_imgs.extend(out_imgs)
+            yield generated_imgs, text_output
+
+    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()
+
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    set_init_device_flags()
+    model_id = (
+        args.hf_model_id
+        if "inpaint" in args.hf_model_id
+        else "stabilityai/stable-diffusion-2-inpainting"
+    )
+    schedulers = get_schedulers(model_id)
+    scheduler_obj = schedulers[args.scheduler]
+    seed = args.seed
+    image = Image.open(args.img_path)
+    mask_image = Image.open(args.mask_path)
+
+    inpaint_obj = InpaintPipeline.from_pretrained(
+        scheduler=scheduler_obj,
+        import_mlir=args.import_mlir,
+        model_id=args.hf_model_id,
+        ckpt_loc=args.ckpt_loc,
+        precision=args.precision,
+        max_length=args.max_length,
+        batch_size=args.batch_size,
+        height=args.height,
+        width=args.width,
+        use_base_vae=args.use_base_vae,
+        use_tuned=args.use_tuned,
+        custom_vae=args.custom_vae,
+        low_cpu_mem_usage=args.low_cpu_mem_usage,
+        debug=args.import_debug if args.import_mlir else False,
+        use_lora=args.use_lora,
+    )
+
+    for current_batch in range(args.batch_count):
+        if current_batch > 0:
+            seed = -1
+        seed = utils.sanitize_seed(seed)
+
+        start_time = time.time()
+        generated_imgs = inpaint_obj.generate_images(
+            args.prompts,
+            args.negative_prompts,
+            image,
+            mask_image,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.inpaint_full_res,
+            args.inpaint_full_res_padding,
+            args.steps,
+            args.guidance_scale,
+            seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        total_time = time.time() - start_time
+        text_output = f"prompt={args.prompts}"
+        text_output += f"\nnegative prompt={args.negative_prompts}"
+        text_output += (
+            f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+        )
+        text_output += f"\nscheduler={args.scheduler}, device={args.device}"
+        text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
+        text_output += (
+            f", batch size={args.batch_size}, max_length={args.max_length}"
+        )
+        text_output += inpaint_obj.log
+        text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+        save_output_img(generated_imgs[0], seed)
+        print(text_output)

apps/stable_diffusion/scripts/outpaint.py

@@ -0,0 +1,312 @@
+import torch
+import time
+from PIL import Image
+from apps.stable_diffusion.src import (
+    args,
+    OutpaintPipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+from apps.stable_diffusion.src.utils import get_generation_text_info
+
+
+schedulers = None
+
+# set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
+init_iree_vulkan_target_triple = args.iree_vulkan_target_triple
+init_use_tuned = args.use_tuned
+init_import_mlir = args.import_mlir
+
+
+# Exposed to UI.
+def outpaint_inf(
+    prompt: str,
+    negative_prompt: str,
+    init_image,
+    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,
+    lora_weights: str,
+    lora_hf_id: str,
+):
+    from apps.stable_diffusion.web.ui.utils import (
+        get_custom_model_pathfile,
+        Config,
+    )
+    import apps.stable_diffusion.web.utils.global_obj as global_obj
+    from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+        SD_STATE_CANCEL,
+    )
+
+    global schedulers
+
+    args.prompts = [prompt]
+    args.negative_prompts = [negative_prompt]
+    args.guidance_scale = guidance_scale
+    args.steps = steps
+    args.scheduler = scheduler
+    args.img_path = "not none"
+
+    # 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 = get_custom_model_pathfile(custom_model)
+    else:
+        args.hf_model_id = custom_model
+
+    use_lora = ""
+    if lora_weights == "None" and not lora_hf_id:
+        use_lora = ""
+    elif not lora_hf_id:
+        use_lora = lora_weights
+    else:
+        use_lora = lora_hf_id
+    args.use_lora = use_lora
+
+    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(
+        "outpaint",
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        height,
+        width,
+        device,
+        use_lora=use_lora,
+        use_stencil=None,
+    )
+    if (
+        not global_obj.get_sd_obj()
+        or global_obj.get_cfg_obj() != new_config_obj
+    ):
+        global_obj.clear_cache()
+        global_obj.set_cfg_obj(new_config_obj)
+        args.precision = precision
+        args.batch_count = batch_count
+        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 = init_iree_vulkan_target_triple
+        args.use_tuned = init_use_tuned
+        args.import_mlir = init_import_mlir
+        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]
+        global_obj.set_sd_obj(
+            OutpaintPipeline.from_pretrained(
+                scheduler_obj,
+                args.import_mlir,
+                args.hf_model_id,
+                args.ckpt_loc,
+                args.custom_vae,
+                args.precision,
+                args.max_length,
+                args.batch_size,
+                args.height,
+                args.width,
+                args.use_base_vae,
+                args.use_tuned,
+                use_lora=use_lora,
+            )
+        )
+
+    global_obj.set_schedulers(schedulers[scheduler])
+
+    start_time = time.time()
+    global_obj.get_sd_obj().log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+
+    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
+
+    text_output = ""
+    for i in range(batch_count):
+        if i > 0:
+            img_seed = utils.sanitize_seed(-1)
+        out_imgs = global_obj.get_sd_obj().generate_images(
+            prompt,
+            negative_prompt,
+            init_image,
+            pixels,
+            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,
+        )
+        seeds.append(img_seed)
+        total_time = time.time() - start_time
+        text_output = get_generation_text_info(seeds, device)
+        text_output += "\n" + global_obj.get_sd_obj().log
+        text_output += f"\nTotal image(s) generation time: {total_time:.4f}sec"
+
+        if global_obj.get_sd_status() == SD_STATE_CANCEL:
+            break
+        else:
+            save_output_img(out_imgs[0], img_seed)
+            generated_imgs.extend(out_imgs)
+            yield generated_imgs, text_output
+
+    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()
+
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    set_init_device_flags()
+    model_id = (
+        args.hf_model_id
+        if "inpaint" in args.hf_model_id
+        else "stabilityai/stable-diffusion-2-inpainting"
+    )
+    schedulers = get_schedulers(model_id)
+    scheduler_obj = schedulers[args.scheduler]
+    seed = args.seed
+    image = Image.open(args.img_path)
+
+    outpaint_obj = OutpaintPipeline.from_pretrained(
+        scheduler_obj,
+        args.import_mlir,
+        args.hf_model_id,
+        args.ckpt_loc,
+        args.custom_vae,
+        args.precision,
+        args.max_length,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.use_base_vae,
+        args.use_tuned,
+        use_lora=args.use_lora,
+    )
+
+    for current_batch in range(args.batch_count):
+        if current_batch > 0:
+            seed = -1
+        seed = utils.sanitize_seed(seed)
+
+        start_time = time.time()
+        generated_imgs = outpaint_obj.generate_images(
+            args.prompts,
+            args.negative_prompts,
+            image,
+            args.pixels,
+            args.mask_blur,
+            args.left,
+            args.right,
+            args.top,
+            args.bottom,
+            args.noise_q,
+            args.color_variation,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.steps,
+            args.guidance_scale,
+            seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        total_time = time.time() - start_time
+        text_output = f"prompt={args.prompts}"
+        text_output += f"\nnegative prompt={args.negative_prompts}"
+        text_output += (
+            f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+        )
+        text_output += f"\nscheduler={args.scheduler}, device={args.device}"
+        text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
+        text_output += (
+            f", batch size={args.batch_size}, max_length={args.max_length}"
+        )
+        text_output += outpaint_obj.log
+        text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+        # save this information as metadata of output generated image.
+        directions = []
+        if args.left:
+            directions.append("left")
+        if args.right:
+            directions.append("right")
+        if args.top:
+            directions.append("up")
+        if args.bottom:
+            directions.append("down")
+        extra_info = {
+            "PIXELS": args.pixels,
+            "MASK_BLUR": args.mask_blur,
+            "DIRECTIONS": directions,
+            "NOISE_Q": args.noise_q,
+            "COLOR_VARIATION": args.color_variation,
+        }
+        save_output_img(generated_imgs[0], seed, extra_info)
+        print(text_output)

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/train_lora_word.py

@@ -0,0 +1,674 @@
+# Install the required libs
+# pip install -U git+https://github.com/huggingface/diffusers.git
+# pip install accelerate transformers ftfy
+
+# HuggingFace Token
+# YOUR_TOKEN = "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"
+
+
+# Import required libraries
+import itertools
+import math
+import os
+from typing import List
+import random
+import torch_mlir
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.utils.data import Dataset
+
+import PIL
+import logging
+
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    PNDMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from PIL import Image
+from tqdm.auto import tqdm
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from diffusers.loaders import AttnProcsLayers
+from diffusers.models.cross_attention import LoRACrossAttnProcessor
+
+import torch_mlir
+from torch_mlir.dynamo import make_simple_dynamo_backend
+import torch._dynamo as dynamo
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch_mlir_e2e_test.linalg_on_tensors_backends import refbackend
+from shark.shark_inference import SharkInference
+
+torch._dynamo.config.verbose = True
+
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    PNDMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.optimization import get_scheduler
+from diffusers.pipelines.stable_diffusion import (
+    StableDiffusionSafetyChecker,
+)
+from PIL import Image
+from tqdm.auto import tqdm
+from transformers import (
+    CLIPFeatureExtractor,
+    CLIPTextModel,
+    CLIPTokenizer,
+)
+
+from io import BytesIO
+
+from dataclasses import dataclass
+from apps.stable_diffusion.src import (
+    args,
+    get_schedulers,
+    set_init_device_flags,
+    clear_all,
+)
+
+
+# Setup the dataset
+class LoraDataset(Dataset):
+    def __init__(
+        self,
+        data_root,
+        tokenizer,
+        size=512,
+        repeats=100,
+        interpolation="bicubic",
+        set="train",
+        prompt="myloraprompt",
+        center_crop=False,
+    ):
+        self.data_root = data_root
+        self.tokenizer = tokenizer
+        self.size = size
+        self.center_crop = center_crop
+        self.prompt = prompt
+
+        self.image_paths = [
+            os.path.join(self.data_root, file_path)
+            for file_path in os.listdir(self.data_root)
+        ]
+
+        self.num_images = len(self.image_paths)
+        self._length = self.num_images
+
+        if set == "train":
+            self._length = self.num_images * repeats
+
+        self.interpolation = {
+            "linear": PIL.Image.LINEAR,
+            "bilinear": PIL.Image.BILINEAR,
+            "bicubic": PIL.Image.BICUBIC,
+            "lanczos": PIL.Image.LANCZOS,
+        }[interpolation]
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, i):
+        example = {}
+        image = Image.open(self.image_paths[i % self.num_images])
+
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        example["input_ids"] = self.tokenizer(
+            self.prompt,
+            padding="max_length",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids[0]
+
+        # default to score-sde preprocessing
+        img = np.array(image).astype(np.uint8)
+
+        if self.center_crop:
+            crop = min(img.shape[0], img.shape[1])
+            (
+                h,
+                w,
+            ) = (
+                img.shape[0],
+                img.shape[1],
+            )
+            img = img[
+                (h - crop) // 2 : (h + crop) // 2,
+                (w - crop) // 2 : (w + crop) // 2,
+            ]
+
+        image = Image.fromarray(img)
+        image = image.resize(
+            (self.size, self.size), resample=self.interpolation
+        )
+
+        image = np.array(image).astype(np.uint8)
+        image = (image / 127.5 - 1.0).astype(np.float32)
+
+        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
+        return example
+
+
+schedulers = None
+
+
+########## Setting up the model ##########
+def lora_train(
+    prompt: str,
+    height: int,
+    width: int,
+    steps: int,
+    guidance_scale: float,
+    seed: int,
+    batch_count: int,
+    batch_size: int,
+    scheduler: str,
+    custom_model: str,
+    hf_model_id: str,
+    precision: str,
+    device: str,
+    max_length: int,
+    training_images_dir: str,
+    lora_save_dir: str,
+):
+    from apps.stable_diffusion.web.ui.utils import (
+        get_custom_model_pathfile,
+        Config,
+    )
+    import apps.stable_diffusion.web.utils.global_obj as global_obj
+
+    global schedulers
+
+    print(
+        "Note LoRA training is not compatible with the latest torch-mlir branch"
+    )
+    print(
+        "To run LoRA training you'll need this to follow this guide for the torch-mlir branch: https://github.com/nod-ai/SHARK/tree/main/shark/examples/shark_training/stable_diffusion"
+    )
+    torch.manual_seed(seed)
+
+    args.prompts = [prompt]
+    args.steps = steps
+
+    # set ckpt_loc and hf_model_id.
+    types = (
+        ".ckpt",
+        ".safetensors",
+    )  # the tuple of file types
+    args.ckpt_loc = ""
+    args.hf_model_id = ""
+    if custom_model == "None":
+        if not hf_model_id:
+            return (
+                None,
+                "Please provide either custom model or huggingface model ID, both must not be empty",
+            )
+        args.hf_model_id = hf_model_id
+    elif ".ckpt" in custom_model or ".safetensors" in custom_model:
+        args.ckpt_loc = custom_model
+    else:
+        args.hf_model_id = custom_model
+
+    args.training_images_dir = training_images_dir
+    args.lora_save_dir = lora_save_dir
+
+    args.precision = precision
+    args.batch_size = batch_size
+    args.max_length = max_length
+    args.height = height
+    args.width = width
+    args.device = device
+
+    # Load the Stable Diffusion model
+    text_encoder = CLIPTextModel.from_pretrained(
+        args.hf_model_id, subfolder="text_encoder"
+    )
+    vae = AutoencoderKL.from_pretrained(args.hf_model_id, subfolder="vae")
+    unet = UNet2DConditionModel.from_pretrained(
+        args.hf_model_id, subfolder="unet"
+    )
+
+    def freeze_params(params):
+        for param in params:
+            param.requires_grad = False
+
+    # Freeze everything but LoRA
+    freeze_params(vae.parameters())
+    freeze_params(unet.parameters())
+    freeze_params(text_encoder.parameters())
+
+    # Move vae and unet to device
+    vae.to(args.device)
+    unet.to(args.device)
+    text_encoder.to(args.device)
+
+    lora_attn_procs = {}
+    for name in unet.attn_processors.keys():
+        cross_attention_dim = (
+            None
+            if name.endswith("attn1.processor")
+            else unet.config.cross_attention_dim
+        )
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[
+                block_id
+            ]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
+
+        lora_attn_procs[name] = LoRACrossAttnProcessor(
+            hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
+        )
+
+    unet.set_attn_processor(lora_attn_procs)
+    lora_layers = AttnProcsLayers(unet.attn_processors)
+
+    class VaeModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.vae = vae
+
+        def forward(self, input):
+            x = self.vae.encode(input, return_dict=False)[0]
+            return x
+
+    class UnetModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.unet = unet
+
+        def forward(self, x, y, z):
+            return self.unet.forward(x, y, z, return_dict=False)[0]
+
+    shark_vae = VaeModel()
+    shark_unet = UnetModel()
+
+    ####### Creating our training data ########
+
+    tokenizer = CLIPTokenizer.from_pretrained(
+        args.hf_model_id,
+        subfolder="tokenizer",
+    )
+
+    # Let's create the Dataset and Dataloader
+    train_dataset = LoraDataset(
+        data_root=args.training_images_dir,
+        tokenizer=tokenizer,
+        size=vae.sample_size,
+        prompt=args.prompts[0],
+        repeats=100,
+        center_crop=False,
+        set="train",
+    )
+
+    def create_dataloader(train_batch_size=1):
+        return torch.utils.data.DataLoader(
+            train_dataset, batch_size=train_batch_size, shuffle=True
+        )
+
+    # Create noise_scheduler for training
+    noise_scheduler = DDPMScheduler.from_config(
+        args.hf_model_id, subfolder="scheduler"
+    )
+
+    ######## Training ###########
+
+    # Define hyperparameters for our training. If you are not happy with your results,
+    # you can tune the `learning_rate` and the `max_train_steps`
+
+    # Setting up all training args
+    hyperparameters = {
+        "learning_rate": 5e-04,
+        "scale_lr": True,
+        "max_train_steps": steps,
+        "train_batch_size": batch_size,
+        "gradient_accumulation_steps": 1,
+        "gradient_checkpointing": True,
+        "mixed_precision": "fp16",
+        "seed": 42,
+        "output_dir": "sd-concept-output",
+    }
+    # creating output directory
+    cwd = os.getcwd()
+    out_dir = os.path.join(cwd, hyperparameters["output_dir"])
+    while not os.path.exists(str(out_dir)):
+        try:
+            os.mkdir(out_dir)
+        except OSError as error:
+            print("Output directory not created")
+
+    ###### Torch-MLIR Compilation ######
+
+    def _remove_nones(fx_g: torch.fx.GraphModule) -> List[int]:
+        removed_indexes = []
+        for node in fx_g.graph.nodes:
+            if node.op == "output":
+                assert (
+                    len(node.args) == 1
+                ), "Output node must have a single argument"
+                node_arg = node.args[0]
+                if isinstance(node_arg, (list, tuple)):
+                    node_arg = list(node_arg)
+                    node_args_len = len(node_arg)
+                    for i in range(node_args_len):
+                        curr_index = node_args_len - (i + 1)
+                        if node_arg[curr_index] is None:
+                            removed_indexes.append(curr_index)
+                            node_arg.pop(curr_index)
+                    node.args = (tuple(node_arg),)
+                    break
+
+        if len(removed_indexes) > 0:
+            fx_g.graph.lint()
+            fx_g.graph.eliminate_dead_code()
+            fx_g.recompile()
+        removed_indexes.sort()
+        return removed_indexes
+
+    def _unwrap_single_tuple_return(fx_g: torch.fx.GraphModule) -> bool:
+        """
+        Replace tuple with tuple element in functions that return one-element tuples.
+        Returns true if an unwrapping took place, and false otherwise.
+        """
+        unwrapped_tuple = False
+        for node in fx_g.graph.nodes:
+            if node.op == "output":
+                assert (
+                    len(node.args) == 1
+                ), "Output node must have a single argument"
+                node_arg = node.args[0]
+                if isinstance(node_arg, tuple):
+                    if len(node_arg) == 1:
+                        node.args = (node_arg[0],)
+                        unwrapped_tuple = True
+                        break
+
+        if unwrapped_tuple:
+            fx_g.graph.lint()
+            fx_g.recompile()
+        return unwrapped_tuple
+
+    def _returns_nothing(fx_g: torch.fx.GraphModule) -> bool:
+        for node in fx_g.graph.nodes:
+            if node.op == "output":
+                assert (
+                    len(node.args) == 1
+                ), "Output node must have a single argument"
+                node_arg = node.args[0]
+                if isinstance(node_arg, tuple):
+                    return len(node_arg) == 0
+        return False
+
+    def transform_fx(fx_g):
+        for node in fx_g.graph.nodes:
+            if node.op == "call_function":
+                if node.target in [
+                    torch.ops.aten.empty,
+                ]:
+                    # aten.empty should be filled with zeros.
+                    if node.target in [torch.ops.aten.empty]:
+                        with fx_g.graph.inserting_after(node):
+                            new_node = fx_g.graph.call_function(
+                                torch.ops.aten.zero_,
+                                args=(node,),
+                            )
+                            node.append(new_node)
+                            node.replace_all_uses_with(new_node)
+                            new_node.args = (node,)
+
+        fx_g.graph.lint()
+
+    @make_simple_dynamo_backend
+    def refbackend_torchdynamo_backend(
+        fx_graph: torch.fx.GraphModule, example_inputs: List[torch.Tensor]
+    ):
+        # handling usage of empty tensor without initializing
+        transform_fx(fx_graph)
+        fx_graph.recompile()
+        if _returns_nothing(fx_graph):
+            return fx_graph
+        removed_none_indexes = _remove_nones(fx_graph)
+        was_unwrapped = _unwrap_single_tuple_return(fx_graph)
+
+        mlir_module = torch_mlir.compile(
+            fx_graph, example_inputs, output_type="linalg-on-tensors"
+        )
+
+        bytecode_stream = BytesIO()
+        mlir_module.operation.write_bytecode(bytecode_stream)
+        bytecode = bytecode_stream.getvalue()
+
+        shark_module = SharkInference(
+            mlir_module=bytecode, device=args.device, mlir_dialect="tm_tensor"
+        )
+        shark_module.compile()
+
+        def compiled_callable(*inputs):
+            inputs = [x.numpy() for x in inputs]
+            result = shark_module("forward", inputs)
+            if was_unwrapped:
+                result = [
+                    result,
+                ]
+            if not isinstance(result, list):
+                result = torch.from_numpy(result)
+            else:
+                result = tuple(torch.from_numpy(x) for x in result)
+                result = list(result)
+                for removed_index in removed_none_indexes:
+                    result.insert(removed_index, None)
+                result = tuple(result)
+            return result
+
+        return compiled_callable
+
+    def predictions(torch_func, jit_func, batchA, batchB):
+        res = jit_func(batchA.numpy(), batchB.numpy())
+        if res is not None:
+            # prediction = torch.from_numpy(res)
+            prediction = res
+        else:
+            prediction = None
+        return prediction
+
+    logger = logging.getLogger(__name__)
+
+    train_batch_size = hyperparameters["train_batch_size"]
+    gradient_accumulation_steps = hyperparameters[
+        "gradient_accumulation_steps"
+    ]
+    learning_rate = hyperparameters["learning_rate"]
+    if hyperparameters["scale_lr"]:
+        learning_rate = (
+            learning_rate
+            * gradient_accumulation_steps
+            * train_batch_size
+            # * accelerator.num_processes
+        )
+
+    # Initialize the optimizer
+    optimizer = torch.optim.AdamW(
+        lora_layers.parameters(),  # only optimize the embeddings
+        lr=learning_rate,
+    )
+
+    # Training function
+    def train_func(batch_pixel_values, batch_input_ids):
+        # Convert images to latent space
+        latents = shark_vae(batch_pixel_values).sample().detach()
+        latents = latents * 0.18215
+
+        # Sample noise that we'll add to the latents
+        noise = torch.randn_like(latents)
+        bsz = latents.shape[0]
+        # Sample a random timestep for each image
+        timesteps = torch.randint(
+            0,
+            noise_scheduler.num_train_timesteps,
+            (bsz,),
+            device=latents.device,
+        ).long()
+
+        # Add noise to the latents according to the noise magnitude at each timestep
+        # (this is the forward diffusion process)
+        noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+        # Get the text embedding for conditioning
+        encoder_hidden_states = text_encoder(batch_input_ids)[0]
+
+        # Predict the noise residual
+        noise_pred = shark_unet(
+            noisy_latents,
+            timesteps,
+            encoder_hidden_states,
+        )
+
+        # Get the target for loss depending on the prediction type
+        if noise_scheduler.config.prediction_type == "epsilon":
+            target = noise
+        elif noise_scheduler.config.prediction_type == "v_prediction":
+            target = noise_scheduler.get_velocity(latents, noise, timesteps)
+        else:
+            raise ValueError(
+                f"Unknown prediction type {noise_scheduler.config.prediction_type}"
+            )
+
+        loss = (
+            F.mse_loss(noise_pred, target, reduction="none")
+            .mean([1, 2, 3])
+            .mean()
+        )
+        loss.backward()
+
+        optimizer.step()
+        optimizer.zero_grad()
+
+        return loss
+
+    def training_function():
+        max_train_steps = hyperparameters["max_train_steps"]
+        output_dir = hyperparameters["output_dir"]
+        gradient_checkpointing = hyperparameters["gradient_checkpointing"]
+
+        train_dataloader = create_dataloader(train_batch_size)
+
+        # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+        num_update_steps_per_epoch = math.ceil(
+            len(train_dataloader) / gradient_accumulation_steps
+        )
+        num_train_epochs = math.ceil(
+            max_train_steps / num_update_steps_per_epoch
+        )
+
+        # Train!
+        total_batch_size = (
+            train_batch_size
+            * gradient_accumulation_steps
+            # train_batch_size * accelerator.num_processes * gradient_accumulation_steps
+        )
+
+        logger.info("***** Running training *****")
+        logger.info(f"  Num examples = {len(train_dataset)}")
+        logger.info(
+            f"  Instantaneous batch size per device = {train_batch_size}"
+        )
+        logger.info(
+            f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
+        )
+        logger.info(
+            f"  Gradient Accumulation steps = {gradient_accumulation_steps}"
+        )
+        logger.info(f"  Total optimization steps = {max_train_steps}")
+        # Only show the progress bar once on each machine.
+        progress_bar = tqdm(
+            # range(max_train_steps), disable=not accelerator.is_local_main_process
+            range(max_train_steps)
+        )
+        progress_bar.set_description("Steps")
+        global_step = 0
+
+        params__ = [
+            i for i in text_encoder.get_input_embeddings().parameters()
+        ]
+
+        for epoch in range(num_train_epochs):
+            unet.train()
+            for step, batch in enumerate(train_dataloader):
+                dynamo_callable = dynamo.optimize(
+                    refbackend_torchdynamo_backend
+                )(train_func)
+                lam_func = lambda x, y: dynamo_callable(
+                    torch.from_numpy(x), torch.from_numpy(y)
+                )
+                loss = predictions(
+                    train_func,
+                    lam_func,
+                    batch["pixel_values"],
+                    batch["input_ids"],
+                )
+
+                # Checks if the accelerator has performed an optimization step behind the scenes
+                progress_bar.update(1)
+                global_step += 1
+
+                logs = {"loss": loss.detach().item()}
+                progress_bar.set_postfix(**logs)
+
+                if global_step >= max_train_steps:
+                    break
+
+    training_function()
+
+    # Save the lora weights
+    unet.save_attn_procs(args.lora_save_dir)
+
+    for param in itertools.chain(unet.parameters(), text_encoder.parameters()):
+        if param.grad is not None:
+            del param.grad  # free some memory
+        torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+    if args.clear_all:
+        clear_all()
+
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    set_init_device_flags()
+    schedulers = get_schedulers(args.hf_model_id)
+    scheduler_obj = schedulers[args.scheduler]
+    seed = args.seed
+    if len(args.prompts) != 1:
+        print("Need exactly one prompt for the LoRA word")
+    lora_train(
+        args.prompts[0],
+        args.height,
+        args.width,
+        args.training_steps,
+        args.guidance_scale,
+        args.seed,
+        args.batch_count,
+        args.batch_size,
+        args.scheduler,
+        "None",
+        args.hf_model_id,
+        args.precision,
+        args.device,
+        args.max_length,
+        args.training_images_dir,
+        args.lora_save_dir,
+    )

apps/stable_diffusion/scripts/txt2img.py

@@ -0,0 +1,258 @@
+import torch
+import time
+from apps.stable_diffusion.src import (
+    args,
+    Text2ImagePipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+from apps.stable_diffusion.src.utils import get_generation_text_info
+
+schedulers = None
+
+# set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
+init_iree_vulkan_target_triple = args.iree_vulkan_target_triple
+init_use_tuned = args.use_tuned
+init_import_mlir = args.import_mlir
+
+
+# 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,
+    lora_weights: str,
+    lora_hf_id: str,
+):
+    from apps.stable_diffusion.web.ui.utils import (
+        get_custom_model_pathfile,
+        Config,
+    )
+    import apps.stable_diffusion.web.utils.global_obj as global_obj
+    from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+        SD_STATE_CANCEL,
+    )
+
+    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 = get_custom_model_pathfile(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
+
+    use_lora = ""
+    if lora_weights == "None" and not lora_hf_id:
+        use_lora = ""
+    elif not lora_hf_id:
+        use_lora = lora_weights
+    else:
+        use_lora = lora_hf_id
+    args.use_lora = use_lora
+
+    dtype = torch.float32 if precision == "fp32" else torch.half
+    cpu_scheduling = not scheduler.startswith("Shark")
+    new_config_obj = Config(
+        "txt2img",
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        height,
+        width,
+        device,
+        use_lora=use_lora,
+        use_stencil=None,
+    )
+    if (
+        not global_obj.get_sd_obj()
+        or global_obj.get_cfg_obj() != new_config_obj
+    ):
+        global_obj.clear_cache()
+        global_obj.set_cfg_obj(new_config_obj)
+        args.precision = precision
+        args.batch_count = batch_count
+        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 = init_iree_vulkan_target_triple
+        args.use_tuned = init_use_tuned
+        args.import_mlir = init_import_mlir
+        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]
+        global_obj.set_sd_obj(
+            Text2ImagePipeline.from_pretrained(
+                scheduler=scheduler_obj,
+                import_mlir=args.import_mlir,
+                model_id=args.hf_model_id,
+                ckpt_loc=args.ckpt_loc,
+                precision=args.precision,
+                max_length=args.max_length,
+                batch_size=args.batch_size,
+                height=args.height,
+                width=args.width,
+                use_base_vae=args.use_base_vae,
+                use_tuned=args.use_tuned,
+                custom_vae=args.custom_vae,
+                low_cpu_mem_usage=args.low_cpu_mem_usage,
+                debug=args.import_debug if args.import_mlir else False,
+                use_lora=use_lora,
+            )
+        )
+
+    global_obj.set_schedulers(schedulers[scheduler])
+
+    start_time = time.time()
+    global_obj.get_sd_obj().log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+    text_output = ""
+    for i in range(batch_count):
+        if i > 0:
+            img_seed = utils.sanitize_seed(-1)
+        out_imgs = global_obj.get_sd_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,
+        )
+        seeds.append(img_seed)
+        total_time = time.time() - start_time
+        text_output = get_generation_text_info(seeds, device)
+        text_output += "\n" + global_obj.get_sd_obj().log
+        text_output += f"\nTotal image(s) generation time: {total_time:.4f}sec"
+
+        if global_obj.get_sd_status() == SD_STATE_CANCEL:
+            break
+        else:
+            save_output_img(out_imgs[0], img_seed)
+            generated_imgs.extend(out_imgs)
+            yield generated_imgs, text_output
+
+    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
+    use_lora = args.use_lora
+    txt2img_obj = Text2ImagePipeline.from_pretrained(
+        scheduler=scheduler_obj,
+        import_mlir=args.import_mlir,
+        model_id=args.hf_model_id,
+        ckpt_loc=args.ckpt_loc,
+        precision=args.precision,
+        max_length=args.max_length,
+        batch_size=args.batch_size,
+        height=args.height,
+        width=args.width,
+        use_base_vae=args.use_base_vae,
+        use_tuned=args.use_tuned,
+        custom_vae=args.custom_vae,
+        low_cpu_mem_usage=args.low_cpu_mem_usage,
+        debug=args.import_debug if args.import_mlir else False,
+        use_lora=use_lora,
+    )
+
+    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/scripts/upscaler.py

@@ -0,0 +1,269 @@
+import torch
+import time
+from PIL import Image
+from apps.stable_diffusion.src import (
+    args,
+    UpscalerPipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+
+
+schedulers = None
+
+# set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
+init_iree_vulkan_target_triple = args.iree_vulkan_target_triple
+init_use_tuned = args.use_tuned
+init_import_mlir = args.import_mlir
+
+
+# Exposed to UI.
+def upscaler_inf(
+    prompt: str,
+    negative_prompt: str,
+    init_image,
+    height: int,
+    width: int,
+    steps: int,
+    noise_level: 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,
+):
+    from apps.stable_diffusion.web.ui.utils import (
+        get_custom_model_pathfile,
+        Config,
+    )
+    import apps.stable_diffusion.web.utils.global_obj as global_obj
+
+    global schedulers
+
+    args.prompts = [prompt]
+    args.negative_prompts = [negative_prompt]
+    args.guidance_scale = guidance_scale
+    args.seed = seed
+    args.steps = steps
+    args.scheduler = scheduler
+
+    if init_image is None:
+        return None, "An Initial Image is required"
+    image = init_image.convert("RGB").resize((height, width))
+
+    # 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 = get_custom_model_pathfile(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")
+    args.height = 128
+    args.width = 128
+    new_config_obj = Config(
+        "upscaler",
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        args.height,
+        args.width,
+        device,
+        use_lora=None,
+        use_stencil=None,
+    )
+    if (
+        not global_obj.get_sd_obj()
+        or global_obj.get_cfg_obj() != new_config_obj
+    ):
+        global_obj.clear_cache()
+        global_obj.set_cfg_obj(new_config_obj)
+        args.batch_size = batch_size
+        args.max_length = max_length
+        args.device = device.split("=>", 1)[1].strip()
+        args.iree_vulkan_target_triple = init_iree_vulkan_target_triple
+        args.use_tuned = init_use_tuned
+        args.import_mlir = init_import_mlir
+        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]
+        global_obj.set_sd_obj(
+            UpscalerPipeline.from_pretrained(
+                scheduler_obj,
+                args.import_mlir,
+                args.hf_model_id,
+                args.ckpt_loc,
+                args.custom_vae,
+                args.precision,
+                args.max_length,
+                args.batch_size,
+                args.height,
+                args.width,
+                args.use_base_vae,
+                args.use_tuned,
+                low_cpu_mem_usage=args.low_cpu_mem_usage,
+            )
+        )
+
+    global_obj.set_schedulers(schedulers[scheduler])
+    global_obj.get_sd_obj().low_res_scheduler = schedulers["DDPM"]
+
+    start_time = time.time()
+    global_obj.get_sd_obj().log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+    extra_info = {"NOISE LEVEL": noise_level}
+    for current_batch in range(batch_count):
+        if current_batch > 0:
+            img_seed = utils.sanitize_seed(-1)
+        low_res_img = image
+        high_res_img = Image.new("RGB", (height * 4, width * 4))
+
+        for i in range(0, width, 128):
+            for j in range(0, height, 128):
+                box = (j, i, j + 128, i + 128)
+                upscaled_image = global_obj.get_sd_obj().generate_images(
+                    prompt,
+                    negative_prompt,
+                    low_res_img.crop(box),
+                    batch_size,
+                    args.height,
+                    args.width,
+                    steps,
+                    noise_level,
+                    guidance_scale,
+                    img_seed,
+                    args.max_length,
+                    dtype,
+                    args.use_base_vae,
+                    cpu_scheduling,
+                )
+                high_res_img.paste(upscaled_image[0], (j * 4, i * 4))
+
+        save_output_img(high_res_img, img_seed, extra_info)
+        generated_imgs.append(high_res_img)
+        seeds.append(img_seed)
+        global_obj.get_sd_obj().log += "\n"
+        yield generated_imgs, global_obj.get_sd_obj().log
+
+    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}, noise_level={noise_level}, 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 += global_obj.get_sd_obj().log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    yield 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
+
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    set_init_device_flags()
+    schedulers = get_schedulers(args.hf_model_id)
+
+    scheduler_obj = schedulers[args.scheduler]
+    image = (
+        Image.open(args.img_path)
+        .convert("RGB")
+        .resize((args.height, args.width))
+    )
+    seed = utils.sanitize_seed(args.seed)
+    # Adjust for height and width based on model
+
+    upscaler_obj = UpscalerPipeline.from_pretrained(
+        scheduler_obj,
+        args.import_mlir,
+        args.hf_model_id,
+        args.ckpt_loc,
+        args.custom_vae,
+        args.precision,
+        args.max_length,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.use_base_vae,
+        args.use_tuned,
+        low_cpu_mem_usage=args.low_cpu_mem_usage,
+        ddpm_scheduler=schedulers["DDPM"],
+    )
+
+    start_time = time.time()
+    generated_imgs = upscaler_obj.generate_images(
+        args.prompts,
+        args.negative_prompts,
+        image,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.steps,
+        args.noise_level,
+        args.guidance_scale,
+        seed,
+        args.max_length,
+        dtype,
+        args.use_base_vae,
+        cpu_scheduling,
+    )
+    total_time = time.time() - start_time
+    text_output = f"prompt={args.prompts}"
+    text_output += f"\nnegative prompt={args.negative_prompts}"
+    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+    text_output += f"\nscheduler={args.scheduler}, device={args.device}"
+    text_output += f"\nsteps={args.steps}, noise_level={args.noise_level}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
+    text_output += (
+        f", batch size={args.batch_size}, max_length={args.max_length}"
+    )
+    text_output += upscaler_obj.log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    extra_info = {"NOISE LEVEL": args.noise_level}
+    save_output_img(generated_imgs[0], seed, extra_info)
+    print(text_output)

apps/stable_diffusion/shark_sd.spec

@@ -0,0 +1,84 @@
+# -*- mode: python ; coding: utf-8 -*-
+from PyInstaller.utils.hooks import collect_data_files
+from PyInstaller.utils.hooks import copy_metadata
+from PyInstaller.utils.hooks import collect_submodules
+
+import sys ; sys.setrecursionlimit(sys.getrecursionlimit() * 5)
+
+datas = []
+datas += collect_data_files('torch')
+datas += copy_metadata('torch')
+datas += copy_metadata('tqdm')
+datas += copy_metadata('regex')
+datas += copy_metadata('requests')
+datas += copy_metadata('packaging')
+datas += copy_metadata('filelock')
+datas += copy_metadata('numpy')
+datas += copy_metadata('tokenizers')
+datas += copy_metadata('importlib_metadata')
+datas += copy_metadata('torch-mlir')
+datas += copy_metadata('omegaconf')
+datas += copy_metadata('safetensors')
+datas += collect_data_files('diffusers')
+datas += collect_data_files('transformers')
+datas += collect_data_files('pytorch_lightning')
+datas += collect_data_files('opencv-python')
+datas += collect_data_files('skimage')
+datas += collect_data_files('gradio')
+datas += collect_data_files('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
+
+hiddenimports = ['shark', 'shark.shark_inference', 'apps']
+hiddenimports += [x for x in collect_submodules("skimage") if "tests" not in x]
+
+a = Analysis(
+    ['web/index.py'],
+    pathex=['.'],
+    binaries=binaries,
+    datas=datas,
+    hiddenimports=hiddenimports,
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+    noarchive=False,
+)
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    [],
+    name='shark_sd',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)

apps/stable_diffusion/shark_sd_cli.spec

@@ -0,0 +1,82 @@
+# -*- mode: python ; coding: utf-8 -*-
+from PyInstaller.utils.hooks import collect_data_files
+from PyInstaller.utils.hooks import collect_submodules
+from PyInstaller.utils.hooks import copy_metadata
+
+import sys ; sys.setrecursionlimit(sys.getrecursionlimit() * 5)
+
+datas = []
+datas += collect_data_files('torch')
+datas += copy_metadata('torch')
+datas += copy_metadata('tqdm')
+datas += copy_metadata('regex')
+datas += copy_metadata('requests')
+datas += copy_metadata('packaging')
+datas += copy_metadata('filelock')
+datas += copy_metadata('numpy')
+datas += copy_metadata('tokenizers')
+datas += copy_metadata('importlib_metadata')
+datas += copy_metadata('torch-mlir')
+datas += copy_metadata('omegaconf')
+datas += copy_metadata('safetensors')
+datas += collect_data_files('diffusers')
+datas += collect_data_files('transformers')
+datas += collect_data_files('opencv-python')
+datas += collect_data_files('pytorch_lightning')
+datas += collect_data_files('skimage')
+datas += collect_data_files('gradio')
+datas += collect_data_files('iree')
+datas += collect_data_files('google-cloud-storage')
+datas += collect_data_files('shark')
+datas += [
+         ( 'src/utils/resources/prompts.json', 'resources' ),
+         ( 'src/utils/resources/model_db.json', 'resources' ),
+         ( 'src/utils/resources/opt_flags.json', 'resources' ),
+         ( 'src/utils/resources/base_model.json', 'resources' ),
+         ]
+
+binaries = []
+
+block_cipher = None
+
+hiddenimports = ['shark', 'shark.shark_inference', 'apps']
+hiddenimports += [x for x in collect_submodules("skimage") if "tests" not in x]
+
+a = Analysis(
+    ['scripts/txt2img.py'],
+    pathex=['.'],
+    binaries=binaries,
+    datas=datas,
+    hiddenimports=hiddenimports,
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+    noarchive=False,
+)
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    [],
+    name='shark_sd_cli',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)

apps/stable_diffusion/src/__init__.py

@@ -0,0 +1,17 @@
+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,
+    StencilPipeline,
+    UpscalerPipeline,
+)
+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 +1,665 @@
+from diffusers import AutoencoderKL, UNet2DConditionModel, ControlNetModel
+from transformers import CLIPTextModel
+from collections import defaultdict
+import torch
+import safetensors.torch
+import traceback
+import sys
+import os
+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,
+    get_stencil_model_id,
+)
+
+
+# These shapes are parameter dependent.
+def replace_shape_str(shape, max_len, width, height, batch_size):
+    new_shape = []
+    for i in range(len(shape)):
+        if shape[i] == "max_len":
+            new_shape.append(max_len)
+        elif shape[i] == "height":
+            new_shape.append(height)
+        elif shape[i] == "width":
+            new_shape.append(width)
+        elif isinstance(shape[i], str):
+            if "*" in shape[i]:
+                mul_val = int(shape[i].split("*")[0])
+                if "batch_size" in shape[i]:
+                    new_shape.append(batch_size * mul_val)
+                elif "height" in shape[i]:
+                    new_shape.append(height * mul_val)
+                elif "width" in shape[i]:
+                    new_shape.append(width * mul_val)
+            elif "/" in shape[i]:
+                import math
+                div_val = int(shape[i].split("/")[1])
+                if "batch_size" in shape[i]:
+                    new_shape.append(math.ceil(batch_size / div_val))
+                elif "height" in shape[i]:
+                    new_shape.append(math.ceil(height / div_val))
+                elif "width" in shape[i]:
+                    new_shape.append(math.ceil(width / div_val))
+        else:
+            new_shape.append(shape[i])
+    return new_shape
+
+
+# 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,
+        debug: bool = False,
+        sharktank_dir: str = "",
+        generate_vmfb: bool = True,
+        is_inpaint: bool = False,
+        is_upscaler: bool = False,
+        use_stencil: str = None,
+        use_lora: str = ""
+    ):
+        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
+        )
+        print(f'use_tuned? sharkify: {use_tuned}')
+        self.use_tuned = use_tuned
+        if use_tuned:
+            self.model_name = self.model_name + "_tuned"
+        self.model_name = self.model_name + "_" + get_path_stem(self.model_id)
+        self.low_cpu_mem_usage = low_cpu_mem_usage
+        self.is_inpaint = is_inpaint
+        self.is_upscaler = is_upscaler
+        self.use_stencil = get_stencil_model_id(use_stencil)
+        if use_lora != "":
+            self.model_name = self.model_name + "_" + get_path_stem(use_lora)
+        self.use_lora = use_lora
+
+        print(self.model_name)
+        self.debug = debug
+        self.sharktank_dir = sharktank_dir
+        self.generate_vmfb = generate_vmfb
+
+    def get_extended_name_for_all_model(self, mask_to_fetch):
+        model_name = {}
+        sub_model_list = ["clip", "unet", "stencil_unet", "vae", "vae_encode", "stencil_adaptor"]
+        index = 0
+        for model in sub_model_list:
+            if mask_to_fetch[index] == False:
+                index += 1
+                continue
+            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)
+            index += 1
+        return model_name
+
+    def check_params(self, max_len, width, height):
+        if not (max_len >= 32 and max_len <= 77):
+            sys.exit("please specify max_len in the range [32, 77].")
+        if not (width % 8 == 0 and width >= 128):
+            sys.exit("width should be greater than 128 and multiple of 8")
+        if not (height % 8 == 0 and height >= 128):
+            sys.exit("height should be greater than 128 and multiple of 8")
+
+    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
+        save_dir = os.path.join(self.sharktank_dir, self.model_name["vae"])
+        if self.debug:
+            os.makedirs(save_dir, exist_ok=True)
+        shark_vae = compile_through_fx(
+            vae,
+            inputs,
+            is_f16=is_f16,
+            use_tuned=self.use_tuned,
+            model_name=self.model_name["vae"],
+            debug=self.debug,
+            generate_vmfb=self.generate_vmfb,
+            save_dir=save_dir,
+            extra_args=get_opt_flags("vae", precision=self.precision),
+        )
+        return shark_vae
+
+    def get_vae_upscaler(self):
+        class VaeModel(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):
+                x = self.vae.decode(input, return_dict=False)[0]
+                x = (x / 2 + 0.5).clamp(0, 1)
+                return x
+
+        vae = VaeModel(low_cpu_mem_usage=self.low_cpu_mem_usage)
+        inputs = tuple(self.inputs["vae"])
+        shark_vae = compile_through_fx(
+            vae,
+            inputs,
+            use_tuned=self.use_tuned,
+            model_name=self.model_name["vae"],
+            extra_args=get_opt_flags("vae", precision="fp32"),
+        )
+        return shark_vae
+
+    def get_controlled_unet(self):
+        class ControlledUnetModel(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, control1,
+                         control2, control3, control4, control5, control6, control7,
+                         control8, control9, control10, control11, control12, control13,
+            ):
+                # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+                db_res_samples = tuple([ control1, control2, control3, control4, control5, control6, control7, control8, control9, control10, control11, control12,])
+                mb_res_samples = control13
+                latents = torch.cat([latent] * 2)
+                unet_out = self.unet.forward(
+                    latents,
+                    timestep,
+                    encoder_hidden_states=text_embedding,
+                    down_block_additional_residuals=db_res_samples,
+                    mid_block_additional_residual=mb_res_samples,
+                    return_dict=False,
+                )[0]
+                noise_pred_uncond, noise_pred_text = unet_out.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (
+                    noise_pred_text - noise_pred_uncond
+                )
+                return noise_pred
+
+        unet = ControlledUnetModel(low_cpu_mem_usage=self.low_cpu_mem_usage)
+        is_f16 = True if self.precision == "fp16" else False
+
+        inputs = tuple(self.inputs["stencil_unet"])
+        input_mask = [True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True,]
+        shark_controlled_unet = compile_through_fx(
+            unet,
+            inputs,
+            model_name=self.model_name["stencil_unet"],
+            is_f16=is_f16,
+            f16_input_mask=input_mask,
+            use_tuned=self.use_tuned,
+            extra_args=get_opt_flags("unet", precision=self.precision),
+        )
+        return shark_controlled_unet
+
+    def get_control_net(self):
+        class StencilControlNetModel(torch.nn.Module):
+            def __init__(
+                self, model_id=self.use_stencil, low_cpu_mem_usage=False
+            ):
+                super().__init__()
+                self.cnet = ControlNetModel.from_pretrained(
+                    model_id,
+                    low_cpu_mem_usage=low_cpu_mem_usage,
+                )
+                self.in_channels = self.cnet.in_channels
+                self.train(False)
+
+            def forward(
+                self,
+                latent,
+                timestep,
+                text_embedding,
+                stencil_image_input,
+            ):
+                # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+                # TODO: guidance NOT NEEDED change in `get_input_info` later
+                latents = torch.cat(
+                    [latent] * 2
+                )  # needs to be same as controlledUNET latents
+                stencil_image = torch.cat(
+                    [stencil_image_input] * 2
+                )  # needs to be same as controlledUNET latents
+                down_block_res_samples, mid_block_res_sample = self.cnet.forward(
+                    latents,
+                    timestep,
+                    encoder_hidden_states=text_embedding,
+                    controlnet_cond=stencil_image,
+                    return_dict=False,
+                )
+                return tuple(list(down_block_res_samples) + [mid_block_res_sample])
+
+        scnet = StencilControlNetModel(low_cpu_mem_usage=self.low_cpu_mem_usage)
+        is_f16 = True if self.precision == "fp16" else False
+
+        inputs = tuple(self.inputs["stencil_adaptor"])
+        input_mask = [True, True, True, True]
+        shark_cnet = compile_through_fx(
+            scnet,
+            inputs,
+            model_name=self.model_name["stencil_adaptor"],
+            is_f16=is_f16,
+            f16_input_mask=input_mask,
+            use_tuned=self.use_tuned,
+            extra_args=get_opt_flags("unet", precision=self.precision),
+        )
+        return shark_cnet
+
+    def get_unet(self):
+        class UnetModel(torch.nn.Module):
+            def __init__(self, model_id=self.model_id, low_cpu_mem_usage=False, use_lora=self.use_lora):
+                super().__init__()
+                self.unet = UNet2DConditionModel.from_pretrained(
+                    model_id,
+                    subfolder="unet",
+                    low_cpu_mem_usage=low_cpu_mem_usage,
+                )
+                if use_lora != "":
+                    self.unet.load_attn_procs(use_lora)
+                self.in_channels = self.unet.in_channels
+                self.train(False)
+                if(args.attention_slicing is not None and args.attention_slicing != "none"):
+                    if(args.attention_slicing.isdigit()):
+                        self.unet.set_attention_slice(int(args.attention_slicing))
+                    else:
+                        self.unet.set_attention_slice(args.attention_slicing)
+
+            # TODO: Instead of flattening the `control` try to use the list.
+            def forward(
+                self, latent, timestep, text_embedding, guidance_scale,
+            ):
+                # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+                latents = torch.cat([latent] * 2)
+                unet_out = self.unet.forward(
+                    latents, timestep, text_embedding, return_dict=False
+                )[0]
+                noise_pred_uncond, noise_pred_text = unet_out.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (
+                    noise_pred_text - noise_pred_uncond
+                )
+                return noise_pred
+
+        unet = UnetModel(low_cpu_mem_usage=self.low_cpu_mem_usage)
+        is_f16 = True if self.precision == "fp16" else False
+        inputs = tuple(self.inputs["unet"])
+        input_mask = [True, True, True, False]
+        save_dir = os.path.join(self.sharktank_dir, self.model_name["unet"])
+        if self.debug:
+            os.makedirs(
+                save_dir,
+                exist_ok=True,
+            )
+        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,
+            debug=self.debug,
+            generate_vmfb=self.generate_vmfb,
+            save_dir=save_dir,
+            extra_args=get_opt_flags("unet", precision=self.precision),
+        )
+        return shark_unet
+
+    def get_unet_upscaler(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, noise_level):
+                unet_out = self.unet.forward(
+                    latent,
+                    timestep,
+                    text_embedding,
+                    noise_level,
+                    return_dict=False,
+                )[0]
+                return unet_out
+
+        unet = UnetModel(low_cpu_mem_usage=self.low_cpu_mem_usage)
+        is_f16 = True if self.precision == "fp16" else False
+        inputs = tuple(self.inputs["unet"])
+        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)
+        save_dir = os.path.join(self.sharktank_dir, self.model_name["clip"])
+        if self.debug:
+            os.makedirs(
+                save_dir,
+                exist_ok=True,
+            )
+        shark_clip = compile_through_fx(
+            clip_model,
+            tuple(self.inputs["clip"]),
+            model_name=self.model_name["clip"],
+            debug=self.debug,
+            generate_vmfb=self.generate_vmfb,
+            save_dir=save_dir,
+            extra_args=get_opt_flags("clip", precision="fp32"),
+        )
+        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, need_stencil):
+        self.inputs = get_input_info(
+            base_models[base_model_id],
+            self.max_len,
+            self.width,
+            self.height,
+            self.batch_size,
+        )
+        if self.is_upscaler:
+            return self.get_clip(), self.get_unet_upscaler(), self.get_vae_upscaler()
+
+        compiled_controlnet = None
+        compiled_controlled_unet = None
+        compiled_unet = None
+        if need_stencil:
+            compiled_controlnet = self.get_control_net()
+            compiled_controlled_unet = self.get_controlled_unet()
+        else:
+            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_stencil:
+            return compiled_clip, compiled_controlled_unet, compiled_vae, compiled_controlnet
+        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, need_stencil = False, False
+        if not self.is_upscaler and args.img_path is not None:
+            if self.use_stencil is not None:
+                need_stencil = True
+            else:
+                need_vae_encode = True
+        # `mask_to_fetch` prepares a mask to pick a combination out of :-
+        # ["clip", "unet", "stencil_unet", "vae", "vae_encode", "stencil_adaptor"]
+        mask_to_fetch = [True, True, False, True, False, False]
+        if need_vae_encode:
+            mask_to_fetch = [True, True, False, True, True, False]
+        elif need_stencil:
+            mask_to_fetch = [True, False, True, True, False, True]
+        self.model_name = self.get_extended_name_for_all_model(mask_to_fetch)
+        vmfbs = fetch_or_delete_vmfbs(self.model_name, self.precision)   
+        if vmfbs[0]:
+            # -- If all vmfbs are indeed present, we also try and fetch the base
+            #    model configuration for running SD with custom checkpoints.
+            if self.custom_weights != "":
+                args.hf_model_id = fetch_and_update_base_model_id(self.custom_weights)
+            if args.hf_model_id == "":
+                sys.exit("Base model configuration for the custom model is missing. Use `--clear_all` and re-run.")
+            print("Loaded vmfbs from cache and successfully fetched base model configuration.")
+            return vmfbs
+
+        # Step 2:
+        # -- If vmfbs weren't found, we try to see if the base model configuration
+        #    for the required SD run is known to us and bypass the retry mechanism.
+        model_to_run = ""
+        if self.custom_weights != "":
+            model_to_run = self.custom_weights
+            assert self.custom_weights.lower().endswith(
+                (".ckpt", ".safetensors")
+            ), "checkpoint files supported can be any of [.ckpt, .safetensors] type"
+            preprocessCKPT(self.custom_weights, self.is_inpaint)
+        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, need_stencil)
+
+        # 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, need_stencil)
+                elif need_stencil:
+                    compiled_clip, compiled_unet, compiled_vae, compiled_controlnet = self.compile_all(model_id, need_vae_encode, need_stencil)
+                else:
+                    compiled_clip, compiled_unet, compiled_vae = self.compile_all(model_id, need_vae_encode, need_stencil)
+            except Exception as e:
+                print(e)
+                print("Retrying with a different base model configuration")
+                continue
+            # -- Once a successful compilation has taken place we'd want to store
+            #    the base model's configuration inferred.
+            fetch_and_update_base_model_id(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,
+                )
+            if need_stencil:
+                return (
+                    compiled_clip,
+                    compiled_unet,
+                    compiled_vae,
+                    compiled_controlnet,
+                )
+            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 +1,18 @@
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_txt2img import (
+    Text2ImagePipeline,
+)
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_img2img import (
+    Image2ImagePipeline,
+)
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_inpaint import (
+    InpaintPipeline,
+)
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_outpaint import (
+    OutpaintPipeline,
+)
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_stencil import (
+    StencilPipeline,
+)
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_upscaler import (
+    UpscalerPipeline,
+)

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_img2img.py

@@ -0,0 +1,172 @@
+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,
+        use_stencil,
+    ):
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get text embeddings 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 +1,445 @@
+import torch
+from tqdm.auto import tqdm
+import numpy as np
+from random import randint
+from PIL import Image, ImageOps
+from transformers import CLIPTokenizer
+from typing import Union
+from shark.shark_inference import SharkInference
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+    DEISMultistepScheduler,
+)
+from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+    StableDiffusionPipeline,
+)
+
+
+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_latents(
+        self,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        dtype,
+    ):
+        latents = torch.randn(
+            (
+                batch_size,
+                4,
+                height // 8,
+                width // 8,
+            ),
+            generator=generator,
+            dtype=torch.float32,
+        ).to(dtype)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def get_crop_region(self, mask, pad=0):
+        h, w = mask.shape
+
+        crop_left = 0
+        for i in range(w):
+            if not (mask[:, i] == 0).all():
+                break
+            crop_left += 1
+
+        crop_right = 0
+        for i in reversed(range(w)):
+            if not (mask[:, i] == 0).all():
+                break
+            crop_right += 1
+
+        crop_top = 0
+        for i in range(h):
+            if not (mask[i] == 0).all():
+                break
+            crop_top += 1
+
+        crop_bottom = 0
+        for i in reversed(range(h)):
+            if not (mask[i] == 0).all():
+                break
+            crop_bottom += 1
+
+        return (
+            int(max(crop_left - pad, 0)),
+            int(max(crop_top - pad, 0)),
+            int(min(w - crop_right + pad, w)),
+            int(min(h - crop_bottom + pad, h)),
+        )
+
+    def expand_crop_region(
+        self,
+        crop_region,
+        processing_width,
+        processing_height,
+        image_width,
+        image_height,
+    ):
+        x1, y1, x2, y2 = crop_region
+
+        ratio_crop_region = (x2 - x1) / (y2 - y1)
+        ratio_processing = processing_width / processing_height
+
+        if ratio_crop_region > ratio_processing:
+            desired_height = (x2 - x1) / ratio_processing
+            desired_height_diff = int(desired_height - (y2 - y1))
+            y1 -= desired_height_diff // 2
+            y2 += desired_height_diff - desired_height_diff // 2
+            if y2 >= image_height:
+                diff = y2 - image_height
+                y2 -= diff
+                y1 -= diff
+            if y1 < 0:
+                y2 -= y1
+                y1 -= y1
+            if y2 >= image_height:
+                y2 = image_height
+        else:
+            desired_width = (y2 - y1) * ratio_processing
+            desired_width_diff = int(desired_width - (x2 - x1))
+            x1 -= desired_width_diff // 2
+            x2 += desired_width_diff - desired_width_diff // 2
+            if x2 >= image_width:
+                diff = x2 - image_width
+                x2 -= diff
+                x1 -= diff
+            if x1 < 0:
+                x2 -= x1
+                x1 -= x1
+            if x2 >= image_width:
+                x2 = image_width
+
+        return x1, y1, x2, y2
+
+    def resize_image(self, resize_mode, im, width, height):
+        """
+        resize_mode:
+            0: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.
+            1: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.
+        """
+
+        if resize_mode == 0:
+            ratio = width / height
+            src_ratio = im.width / im.height
+
+            src_w = (
+                width if ratio > src_ratio else im.width * height // im.height
+            )
+            src_h = (
+                height if ratio <= src_ratio else im.height * width // im.width
+            )
+
+            resized = im.resize((src_w, src_h), resample=Image.LANCZOS)
+            res = Image.new("RGB", (width, height))
+            res.paste(
+                resized,
+                box=(width // 2 - src_w // 2, height // 2 - src_h // 2),
+            )
+
+        else:
+            ratio = width / height
+            src_ratio = im.width / im.height
+
+            src_w = (
+                width if ratio < src_ratio else im.width * height // im.height
+            )
+            src_h = (
+                height if ratio >= src_ratio else im.height * width // im.width
+            )
+
+            resized = im.resize((src_w, src_h), resample=Image.LANCZOS)
+            res = Image.new("RGB", (width, height))
+            res.paste(
+                resized,
+                box=(width // 2 - src_w // 2, height // 2 - src_h // 2),
+            )
+
+            if ratio < src_ratio:
+                fill_height = height // 2 - src_h // 2
+                res.paste(
+                    resized.resize((width, fill_height), box=(0, 0, width, 0)),
+                    box=(0, 0),
+                )
+                res.paste(
+                    resized.resize(
+                        (width, fill_height),
+                        box=(0, resized.height, width, resized.height),
+                    ),
+                    box=(0, fill_height + src_h),
+                )
+            elif ratio > src_ratio:
+                fill_width = width // 2 - src_w // 2
+                res.paste(
+                    resized.resize(
+                        (fill_width, height), box=(0, 0, 0, height)
+                    ),
+                    box=(0, 0),
+                )
+                res.paste(
+                    resized.resize(
+                        (fill_width, height),
+                        box=(resized.width, 0, resized.width, height),
+                    ),
+                    box=(fill_width + src_w, 0),
+                )
+
+        return res
+
+    def prepare_mask_and_masked_image(
+        self,
+        image,
+        mask,
+        height,
+        width,
+        inpaint_full_res,
+        inpaint_full_res_padding,
+    ):
+        # preprocess image
+        image = image.resize((width, height))
+        mask = mask.resize((width, height))
+
+        paste_to = ()
+        overlay_image = None
+        if inpaint_full_res:
+            # prepare overlay image
+            overlay_image = Image.new("RGB", (image.width, image.height))
+            overlay_image.paste(
+                image.convert("RGB"),
+                mask=ImageOps.invert(mask.convert("L")),
+            )
+
+            # prepare mask
+            mask = mask.convert("L")
+            crop_region = self.get_crop_region(
+                np.array(mask), inpaint_full_res_padding
+            )
+            crop_region = self.expand_crop_region(
+                crop_region, width, height, mask.width, mask.height
+            )
+            x1, y1, x2, y2 = crop_region
+            mask = mask.crop(crop_region)
+            mask = self.resize_image(1, mask, width, height)
+            paste_to = (x1, y1, x2 - x1, y2 - y1)
+
+            # prepare image
+            image = image.crop(crop_region)
+            image = self.resize_image(1, image, width, height)
+
+        if isinstance(image, (Image.Image, np.ndarray)):
+            image = [image]
+
+        if isinstance(image, list) and isinstance(image[0], Image.Image):
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+        # preprocess mask
+        if isinstance(mask, (Image.Image, np.ndarray)):
+            mask = [mask]
+
+        if isinstance(mask, list) and isinstance(mask[0], Image.Image):
+            mask = np.concatenate(
+                [np.array(m.convert("L"))[None, None, :] for m in mask], axis=0
+            )
+            mask = mask.astype(np.float32) / 255.0
+        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
+            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
+
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+        mask = torch.from_numpy(mask)
+
+        masked_image = image * (mask < 0.5)
+
+        return mask, masked_image, paste_to, overlay_image
+
+    def prepare_mask_latents(
+        self,
+        mask,
+        masked_image,
+        batch_size,
+        height,
+        width,
+        dtype,
+    ):
+        mask = torch.nn.functional.interpolate(
+            mask, size=(height // 8, width // 8)
+        )
+        mask = mask.to(dtype)
+
+        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 apply_overlay(self, image, paste_loc, overlay):
+        x, y, w, h = paste_loc
+        image = self.resize_image(0, image, w, h)
+        overlay.paste(image, (x, y))
+
+        return overlay
+
+    def generate_images(
+        self,
+        prompts,
+        neg_prompts,
+        image,
+        mask_image,
+        batch_size,
+        height,
+        width,
+        inpaint_full_res,
+        inpaint_full_res_padding,
+        num_inference_steps,
+        guidance_scale,
+        seed,
+        max_length,
+        dtype,
+        use_base_vae,
+        cpu_scheduling,
+    ):
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get initial latents
+        init_latents = self.prepare_latents(
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+            dtype=dtype,
+        )
+
+        # Get text embeddings 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,
+            paste_to,
+            overlay_image,
+        ) = self.prepare_mask_and_masked_image(
+            image,
+            mask_image,
+            height,
+            width,
+            inpaint_full_res,
+            inpaint_full_res_padding,
+        )
+
+        # Prepare mask latent variables
+        mask, masked_image_latents = self.prepare_mask_latents(
+            mask=mask,
+            masked_image=masked_image,
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            dtype=dtype,
+        )
+
+        # Get Image latents
+        latents = self.produce_img_latents(
+            latents=init_latents,
+            text_embeddings=text_embeddings,
+            guidance_scale=guidance_scale,
+            total_timesteps=self.scheduler.timesteps,
+            dtype=dtype,
+            cpu_scheduling=cpu_scheduling,
+            mask=mask,
+            masked_image_latents=masked_image_latents,
+        )
+
+        # Img latents -> PIL images
+        all_imgs = []
+        for i in tqdm(range(0, latents.shape[0], batch_size)):
+            imgs = self.decode_latents(
+                latents=latents[i : i + batch_size],
+                use_base_vae=use_base_vae,
+                cpu_scheduling=cpu_scheduling,
+            )
+            all_imgs.extend(imgs)
+
+        if inpaint_full_res:
+            output_image = self.apply_overlay(
+                all_imgs[0], paste_to, overlay_image
+            )
+            return [output_image]
+
+        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_outpaint.py

@@ -0,0 +1,541 @@
+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)
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_mask_and_masked_image(
+        self, image, mask, mask_blur, width, height
+    ):
+        if mask_blur > 0:
+            mask = mask.filter(ImageFilter.GaussianBlur(mask_blur))
+        image = image.resize((width, height))
+        mask = mask.resize((width, height))
+
+        # preprocess image
+        if isinstance(image, (Image.Image, np.ndarray)):
+            image = [image]
+
+        if isinstance(image, list) and isinstance(image[0], Image.Image):
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+        # preprocess mask
+        if isinstance(mask, (Image.Image, np.ndarray)):
+            mask = [mask]
+
+        if isinstance(mask, list) and isinstance(mask[0], Image.Image):
+            mask = np.concatenate(
+                [np.array(m.convert("L"))[None, None, :] for m in mask], axis=0
+            )
+            mask = mask.astype(np.float32) / 255.0
+        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
+            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
+
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+        mask = torch.from_numpy(mask)
+
+        masked_image = image * (mask < 0.5)
+
+        return mask, masked_image
+
+    def prepare_mask_latents(
+        self,
+        mask,
+        masked_image,
+        batch_size,
+        height,
+        width,
+        dtype,
+    ):
+        mask = torch.nn.functional.interpolate(
+            mask, size=(height // 8, width // 8)
+        )
+        mask = mask.to(dtype)
+
+        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, width, height
+            )
+
+            # Prepare mask latent variables
+            mask, masked_image_latents = self.prepare_mask_latents(
+                mask=mask,
+                masked_image=masked_image,
+                batch_size=batch_size,
+                height=height,
+                width=width,
+                dtype=dtype,
+            )
+
+            # Get Image latents
+            latents = self.produce_img_latents(
+                latents=init_latents,
+                text_embeddings=text_embeddings,
+                guidance_scale=guidance_scale,
+                total_timesteps=self.scheduler.timesteps,
+                dtype=dtype,
+                cpu_scheduling=cpu_scheduling,
+                mask=mask,
+                masked_image_latents=masked_image_latents,
+            )
+
+            # Img latents -> PIL images
+            all_imgs = []
+            for i in tqdm(range(0, latents.shape[0], batch_size)):
+                imgs = self.decode_latents(
+                    latents=latents[i : i + batch_size],
+                    use_base_vae=use_base_vae,
+                    cpu_scheduling=cpu_scheduling,
+                )
+                all_imgs.extend(imgs)
+
+            res_img = all_imgs[0].resize(
+                (image_to_process.width, image_to_process.height)
+            )
+            output_image.paste(
+                res_img,
+                (
+                    0 if is_left else output_image.width - res_img.width,
+                    0 if is_top else output_image.height - res_img.height,
+                ),
+            )
+            output_image = output_image.crop((0, 0, res_w, res_h))
+
+            return output_image
+
+        img = image.resize((width, height))
+        if left > 0:
+            img = expand(img, left, is_left=True)
+        if right > 0:
+            img = expand(img, right, is_right=True)
+        if up > 0:
+            img = expand(img, up, is_top=True)
+        if down > 0:
+            img = expand(img, down, is_bottom=True)
+
+        return [img]

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_stencil.py

@@ -0,0 +1,150 @@
+import torch
+import time
+import numpy as np
+from tqdm.auto import tqdm
+from random import randint
+from PIL import Image
+from transformers import CLIPTokenizer
+from typing import Union
+from shark.shark_inference import SharkInference
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+)
+from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+    StableDiffusionPipeline,
+)
+from apps.stable_diffusion.src.utils import controlnet_hint_conversion
+
+
+class StencilPipeline(StableDiffusionPipeline):
+    def __init__(
+        self,
+        controlnet: SharkInference,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+        ],
+    ):
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler)
+        self.controlnet = controlnet
+
+    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,
+        image,
+        batch_size,
+        height,
+        width,
+        num_inference_steps,
+        strength,
+        guidance_scale,
+        seed,
+        max_length,
+        dtype,
+        use_base_vae,
+        cpu_scheduling,
+        use_stencil,
+    ):
+        # Control Embedding check & conversion
+        # TODO: 1. Change `num_images_per_prompt`.
+        controlnet_hint = controlnet_hint_conversion(
+            image, use_stencil, height, width, dtype, num_images_per_prompt=1
+        )
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get text embeddings 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 initial latent.
+        init_latents = self.prepare_latents(
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+            dtype=dtype,
+        )
+        final_timesteps = self.scheduler.timesteps
+
+        # Get Image latents
+        latents = self.produce_stencil_latents(
+            latents=init_latents,
+            text_embeddings=text_embeddings,
+            guidance_scale=guidance_scale,
+            total_timesteps=final_timesteps,
+            dtype=dtype,
+            cpu_scheduling=cpu_scheduling,
+            controlnet_hint=controlnet_hint,
+            controlnet=self.controlnet,
+        )
+
+        # Img latents -> PIL images
+        all_imgs = []
+        for i in tqdm(range(0, latents.shape[0], batch_size)):
+            imgs = self.decode_latents(
+                latents=latents[i : i + batch_size],
+                use_base_vae=use_base_vae,
+                cpu_scheduling=cpu_scheduling,
+            )
+            all_imgs.extend(imgs)
+
+        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_txt2img.py

@@ -0,0 +1,139 @@
+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_upscaler.py

@@ -0,0 +1,310 @@
+import inspect
+import torch
+import time
+from tqdm.auto import tqdm
+import numpy as np
+from random import randint
+from transformers import CLIPTokenizer
+from typing import Union
+from shark.shark_inference import SharkInference
+from diffusers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    KDPM2DiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+    DEISMultistepScheduler,
+)
+from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+    StableDiffusionPipeline,
+)
+from apps.stable_diffusion.src.utils import (
+    start_profiling,
+    end_profiling,
+)
+from PIL import Image
+
+
+def preprocess(image):
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, Image.Image):
+        image = [image]
+
+    if isinstance(image[0], Image.Image):
+        w, h = image[0].size
+        w, h = map(
+            lambda x: x - x % 64, (w, h)
+        )  # resize to integer multiple of 64
+
+        image = [np.array(i.resize((w, h)))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
+
+
+class UpscalerPipeline(StableDiffusionPipeline):
+    def __init__(
+        self,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+            DEISMultistepScheduler,
+        ],
+        low_res_scheduler: Union[
+            DDIMScheduler,
+            DDPMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+            DEISMultistepScheduler,
+        ],
+    ):
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler)
+        self.low_res_scheduler = low_res_scheduler
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        accepts_eta = "eta" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def decode_latents(self, latents, use_base_vae, cpu_scheduling):
+        latents = 1 / 0.08333 * (latents.float())
+        latents_numpy = latents
+        if cpu_scheduling:
+            latents_numpy = latents.detach().numpy()
+
+        profile_device = start_profiling(file_path="vae.rdc")
+        vae_start = time.time()
+        images = self.vae("forward", (latents_numpy,))
+        vae_inf_time = (time.time() - vae_start) * 1000
+        end_profiling(profile_device)
+        self.log += f"\nVAE Inference time (ms): {vae_inf_time:.3f}"
+
+        images = torch.from_numpy(images)
+        images = (images.detach().cpu() * 255.0).numpy()
+        images = images.round()
+
+        images = torch.from_numpy(images).to(torch.uint8).permute(0, 2, 3, 1)
+        pil_images = [Image.fromarray(image) for image in images.numpy()]
+        return pil_images
+
+    def prepare_latents(
+        self,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        dtype,
+    ):
+        latents = torch.randn(
+            (
+                batch_size,
+                4,
+                height,
+                width,
+            ),
+            generator=generator,
+            dtype=torch.float32,
+        ).to(dtype)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+        self.scheduler.is_scale_input_called = True
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def produce_img_latents(
+        self,
+        latents,
+        image,
+        text_embeddings,
+        guidance_scale,
+        noise_level,
+        total_timesteps,
+        dtype,
+        cpu_scheduling,
+        extra_step_kwargs,
+        return_all_latents=False,
+    ):
+        step_time_sum = 0
+        latent_history = [latents]
+        text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
+        text_embeddings_numpy = text_embeddings.detach().numpy()
+        for i, t in tqdm(enumerate(total_timesteps)):
+            step_start_time = time.time()
+            latent_model_input = torch.cat([latents] * 2)
+            latent_model_input = self.scheduler.scale_model_input(
+                latent_model_input, t
+            )
+            latent_model_input = torch.cat([latent_model_input, image], dim=1)
+            timestep = torch.tensor([t]).to(dtype).detach().numpy()
+            if cpu_scheduling:
+                latent_model_input = latent_model_input.detach().numpy()
+
+            # Profiling Unet.
+            profile_device = start_profiling(file_path="unet.rdc")
+            noise_pred = self.unet(
+                "forward",
+                (
+                    latent_model_input,
+                    timestep,
+                    text_embeddings_numpy,
+                    noise_level,
+                ),
+            )
+            end_profiling(profile_device)
+            noise_pred = torch.from_numpy(noise_pred)
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (
+                noise_pred_text - noise_pred_uncond
+            )
+
+            if cpu_scheduling:
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs
+                ).prev_sample
+            else:
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs
+                )
+
+            latent_history.append(latents)
+            step_time = (time.time() - step_start_time) * 1000
+            #  self.log += (
+            #      f"\nstep = {i} | timestep = {t} | time = {step_time:.2f}ms"
+            #  )
+            step_time_sum += step_time
+
+        avg_step_time = step_time_sum / len(total_timesteps)
+        self.log += f"\nAverage step time: {avg_step_time}ms/it"
+
+        if not return_all_latents:
+            return latents
+        all_latents = torch.cat(latent_history, dim=0)
+        return all_latents
+
+    def generate_images(
+        self,
+        prompts,
+        neg_prompts,
+        image,
+        batch_size,
+        height,
+        width,
+        num_inference_steps,
+        noise_level,
+        guidance_scale,
+        seed,
+        max_length,
+        dtype,
+        use_base_vae,
+        cpu_scheduling,
+    ):
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        # TODO: Wouldn't it be preferable to just report an error instead of modifying the seed on the fly?
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get text embeddings from prompts
+        text_embeddings = self.encode_prompts(prompts, neg_prompts, max_length)
+
+        # 4. Preprocess image
+        image = preprocess(image).to(dtype)
+
+        # 5. Add noise to image
+        noise_level = torch.tensor([noise_level], dtype=torch.long)
+        noise = torch.randn(
+            image.shape,
+            generator=generator,
+        ).to(dtype)
+        image = self.low_res_scheduler.add_noise(image, noise, noise_level)
+        image = torch.cat([image] * 2)
+        noise_level = torch.cat([noise_level] * image.shape[0])
+
+        height, width = image.shape[2:]
+        # Get initial latents
+        init_latents = self.prepare_latents(
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+            dtype=dtype,
+        )
+
+        eta = 0.0
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # guidance scale as a float32 tensor.
+        #  guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
+
+        # Get Image latents
+        latents = self.produce_img_latents(
+            latents=init_latents,
+            image=image,
+            text_embeddings=text_embeddings,
+            guidance_scale=guidance_scale,
+            noise_level=noise_level,
+            total_timesteps=self.scheduler.timesteps,
+            dtype=dtype,
+            cpu_scheduling=cpu_scheduling,
+            extra_step_kwargs=extra_step_kwargs,
+        )
+
+        # Img latents -> PIL images
+        all_imgs = []
+        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 +1,430 @@
+import torch
+import numpy as np
+from transformers import CLIPTokenizer
+from PIL import Image
+from tqdm.auto import tqdm
+import time
+from typing import Union
+from diffusers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    KDPM2DiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+    DEISMultistepScheduler,
+)
+from shark.shark_inference import SharkInference
+from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
+from apps.stable_diffusion.src.models import (
+    SharkifyStableDiffusionModel,
+    get_vae_encode,
+    get_vae,
+    get_clip,
+    get_unet,
+    get_tokenizer,
+)
+from apps.stable_diffusion.src.utils import (
+    start_profiling,
+    end_profiling,
+)
+
+SD_STATE_IDLE = "idle"
+SD_STATE_CANCEL = "cancel"
+
+
+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 = ""
+        self.status = SD_STATE_IDLE
+
+    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_stencil_latents(
+        self,
+        latents,
+        text_embeddings,
+        guidance_scale,
+        total_timesteps,
+        dtype,
+        cpu_scheduling,
+        controlnet_hint=None,
+        controlnet=None,
+        controlnet_conditioning_scale: float = 1.0,
+        mask=None,
+        masked_image_latents=None,
+        return_all_latents=False,
+    ):
+        step_time_sum = 0
+        latent_history = [latents]
+        text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
+        text_embeddings_numpy = text_embeddings.detach().numpy()
+        for i, t in tqdm(enumerate(total_timesteps)):
+            step_start_time = time.time()
+            timestep = torch.tensor([t]).to(dtype)
+            latent_model_input = self.scheduler.scale_model_input(latents, t)
+            if mask is not None and masked_image_latents is not None:
+                latent_model_input = torch.cat(
+                    [
+                        torch.from_numpy(np.asarray(latent_model_input)),
+                        mask,
+                        masked_image_latents,
+                    ],
+                    dim=1,
+                ).to(dtype)
+            if cpu_scheduling:
+                latent_model_input = latent_model_input.detach().numpy()
+
+            if not torch.is_tensor(latent_model_input):
+                latent_model_input_1 = torch.from_numpy(
+                    np.asarray(latent_model_input)
+                ).to(dtype)
+            else:
+                latent_model_input_1 = latent_model_input
+            control = controlnet(
+                "forward",
+                (
+                    latent_model_input_1,
+                    timestep,
+                    text_embeddings,
+                    controlnet_hint,
+                ),
+                send_to_host=False,
+            )
+            timestep = timestep.detach().numpy()
+            # Profiling Unet.
+            profile_device = start_profiling(file_path="unet.rdc")
+            # TODO: Pass `control` as it is to Unet. Same as TODO mentioned in model_wrappers.py.
+            noise_pred = self.unet(
+                "forward",
+                (
+                    latent_model_input,
+                    timestep,
+                    text_embeddings_numpy,
+                    guidance_scale,
+                    control[0],
+                    control[1],
+                    control[2],
+                    control[3],
+                    control[4],
+                    control[5],
+                    control[6],
+                    control[7],
+                    control[8],
+                    control[9],
+                    control[10],
+                    control[11],
+                    control[12],
+                ),
+                send_to_host=False,
+            )
+            end_profiling(profile_device)
+
+            if cpu_scheduling:
+                noise_pred = torch.from_numpy(noise_pred.to_host())
+                latents = self.scheduler.step(
+                    noise_pred, t, latents
+                ).prev_sample
+            else:
+                latents = self.scheduler.step(noise_pred, t, latents)
+
+            latent_history.append(latents)
+            step_time = (time.time() - step_start_time) * 1000
+            #  self.log += (
+            #      f"\nstep = {i} | timestep = {t} | time = {step_time:.2f}ms"
+            #  )
+            step_time_sum += step_time
+
+        avg_step_time = step_time_sum / len(total_timesteps)
+        self.log += f"\nAverage step time: {avg_step_time}ms/it"
+
+        if not return_all_latents:
+            return latents
+        all_latents = torch.cat(latent_history, dim=0)
+        return all_latents
+
+    def produce_img_latents(
+        self,
+        latents,
+        text_embeddings,
+        guidance_scale,
+        total_timesteps,
+        dtype,
+        cpu_scheduling,
+        mask=None,
+        masked_image_latents=None,
+        return_all_latents=False,
+    ):
+        self.status = SD_STATE_IDLE
+        step_time_sum = 0
+        latent_history = [latents]
+        text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
+        text_embeddings_numpy = text_embeddings.detach().numpy()
+        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
+
+            if self.status == SD_STATE_CANCEL:
+                break
+
+        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,
+        debug: bool = False,
+        use_stencil: str = None,
+        use_lora: str = "",
+        ddpm_scheduler: DDPMScheduler = None,
+    ):
+        is_inpaint = cls.__name__ in [
+            "InpaintPipeline",
+            "OutpaintPipeline",
+        ]
+        is_upscaler = cls.__name__ in ["UpscalerPipeline"]
+        if import_mlir or use_lora:
+            if not import_mlir:
+                print(
+                    "Warning: LoRA provided but import_mlir not specified. Importing MLIR anyways."
+                )
+            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,
+                debug=debug,
+                is_inpaint=is_inpaint,
+                is_upscaler=is_upscaler,
+                use_stencil=use_stencil,
+                use_lora=use_lora,
+            )
+            if cls.__name__ in [
+                "Image2ImagePipeline",
+                "InpaintPipeline",
+                "OutpaintPipeline",
+            ]:
+                clip, unet, vae, vae_encode = mlir_import()
+                return cls(
+                    vae_encode, vae, clip, get_tokenizer(), unet, scheduler
+                )
+            if cls.__name__ in ["StencilPipeline"]:
+                clip, unet, vae, controlnet = mlir_import()
+                return cls(
+                    controlnet, vae, clip, get_tokenizer(), unet, scheduler
+                )
+            if cls.__name__ in ["UpscalerPipeline"]:
+                clip, unet, vae = mlir_import()
+                return cls(
+                    vae, clip, get_tokenizer(), unet, scheduler, ddpm_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,
+                )
+            if cls.__name__ == "StencilPipeline":
+                import sys
+
+                sys.exit(
+                    "StencilPipeline not supported with SharkTank currently."
+                )
+            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,
+                is_inpaint=is_inpaint,
+                is_upscaler=is_upscaler,
+            )
+            if cls.__name__ in [
+                "Image2ImagePipeline",
+                "InpaintPipeline",
+                "OutpaintPipeline",
+            ]:
+                clip, unet, vae, vae_encode = mlir_import()
+                return cls(
+                    vae_encode, vae, clip, get_tokenizer(), unet, scheduler
+                )
+            if cls.__name__ == "StencilPipeline":
+                clip, unet, vae, controlnet = mlir_import()
+                return cls(
+                    controlnet, 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 +1,66 @@
+from diffusers import (
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    DDPMScheduler,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    KDPM2DiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DEISMultistepScheduler,
+)
+from apps.stable_diffusion.src.schedulers.shark_eulerdiscrete import (
+    SharkEulerDiscreteScheduler,
+)
+
+
+def get_schedulers(model_id):
+    schedulers = dict()
+    schedulers["PNDM"] = PNDMScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["DDPM"] = DDPMScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["KDPM2Discrete"] = KDPM2DiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["LMSDiscrete"] = LMSDiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["DDIM"] = DDIMScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers[
+        "DPMSolverMultistep"
+    ] = DPMSolverMultistepScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["EulerDiscrete"] = EulerDiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers[
+        "EulerAncestralDiscrete"
+    ] = EulerAncestralDiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["DEISMultistep"] = DEISMultistepScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers[
+        "SharkEulerDiscrete"
+    ] = SharkEulerDiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["SharkEulerDiscrete"].compile()
+    return schedulers

apps/stable_diffusion/src/schedulers/shark_eulerdiscrete.py

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

apps/stable_diffusion/src/utils/__init__.py

@@ -0,0 +1,36 @@
+from apps.stable_diffusion.src.utils.profiler import (
+    start_profiling,
+    end_profiling,
+)
+from apps.stable_diffusion.src.utils.resources import (
+    prompt_examples,
+    models_db,
+    base_models,
+    opt_flags,
+    resource_path,
+)
+from apps.stable_diffusion.src.utils.sd_annotation import sd_model_annotation
+from apps.stable_diffusion.src.utils.stable_args import args
+from apps.stable_diffusion.src.utils.stencils.stencil_utils import (
+    controlnet_hint_conversion,
+    get_stencil_model_id,
+)
+from apps.stable_diffusion.src.utils.utils import (
+    get_shark_model,
+    compile_through_fx,
+    set_iree_runtime_flags,
+    map_device_to_name_path,
+    set_init_device_flags,
+    get_available_devices,
+    get_opt_flags,
+    preprocessCKPT,
+    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,
+    get_generation_text_info,
+)

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 +1,384 @@
+{
+    "stabilityai/stable-diffusion-x4-upscaler": {
+        "unet": {
+            "latents": {
+                "shape": [
+                    "2*batch_size",
+                    7,
+                    "8*height",
+                    "8*width"
+                ],
+                "dtype": "f32"
+            },
+            "timesteps": {
+                "shape": [
+                    1
+                ],
+                "dtype": "f32"
+            },
+            "embedding": {
+                "shape": [
+                    "2*batch_size",
+                    "max_len",
+                    1024
+                ],
+                "dtype": "f32"
+            },
+            "noise_level": {
+                "shape": [2],
+                "dtype": "i64"
+            }
+        },
+        "vae": {
+            "latents" : {
+                "shape" : [
+                    "1*batch_size",4,"8*height","8*width"
+                ],
+                "dtype":"f32"
+            }
+        },
+        "clip": {
+            "token" : {
+                "shape" : [
+                    "2*batch_size",
+                    "max_len"
+                ],
+                "dtype":"i64"
+            }
+        }
+    },
+    "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"
+            }
+        },
+        "stencil_adaptor": {
+            "latents": {
+                "shape": [
+                    "1*batch_size",
+                    4,
+                    "height",
+                    "width"
+                ],
+                "dtype": "f32"
+            },
+            "timesteps": {
+                "shape": [
+                    1
+                ],
+                "dtype": "f32"
+            },
+            "embedding": {
+                "shape": [
+                    "2*batch_size",
+                    "max_len",
+                    768
+                ],
+                "dtype": "f32"
+            },
+            "controlnet_hint": {
+                "shape": [1, 3, "8*height", "8*width"],
+                "dtype": "f32"
+            }
+        },
+        "stencil_unet": {
+            "latents": {
+                "shape": [
+                    "1*batch_size",
+                    4,
+                    "height",
+                    "width"
+                ],
+                "dtype": "f32"
+            },
+            "timesteps": {
+                "shape": [
+                    1
+                ],
+                "dtype": "f32"
+            },
+            "embedding": {
+                "shape": [
+                    "2*batch_size",
+                    "max_len",
+                    768
+                ],
+                "dtype": "f32"
+            },
+            "guidance_scale": {
+                "shape": 2,
+                "dtype": "f32"
+            },
+            "control1": {
+                "shape": [2, 320, "height", "width"],
+                "dtype": "f32"
+            },
+            "control2": {
+                "shape": [2, 320, "height", "width"],
+                "dtype": "f32"
+            },
+            "control3": {
+                "shape": [2, 320, "height", "width"],
+                "dtype": "f32"
+            },
+            "control4": {
+                "shape": [2, 320, "height/2", "width/2"],
+                "dtype": "f32"
+            },
+            "control5": {
+                "shape": [2, 640, "height/2", "width/2"],
+                "dtype": "f32"
+            },
+            "control6": {
+                "shape": [2, 640, "height/2", "width/2"],
+                "dtype": "f32"
+            },
+            "control7": {
+                "shape": [2, 640, "height/4", "width/4"],
+                "dtype": "f32"
+            },
+            "control8": {
+                "shape": [2, 1280, "height/4", "width/4"],
+                "dtype": "f32"
+            },
+            "control9": {
+                "shape": [2, 1280, "height/4", "width/4"],
+                "dtype": "f32"
+            },
+            "control10": {
+                "shape": [2, 1280, "height/8", "width/8"],
+                "dtype": "f32"
+            },
+            "control11": {
+                "shape": [2, 1280, "height/8", "width/8"],
+                "dtype": "f32"
+            },
+            "control12": {
+                "shape": [2, 1280, "height/8", "width/8"],
+                "dtype": "f32"
+            },
+            "control13": {
+                "shape": [2, 1280, "height/8", "width/8"],
+                "dtype": "f32"
+            }
+        },
+        "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"
+            }
+        }
+    },
+    "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"
+            }
+        }
+    }
+}

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 +1,85 @@
+[
+  {
+    "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/unet/fp32/length_64/untuned":"unet_1_64_512_512_fp32_CompVis_stable_diffusion_v1_4",
+    "stablediffusion/v1_4/vae/fp16/length_77/untuned":"vae_19dec_fp16",
+    "stablediffusion/v1_4/vae/fp16/length_77/tuned":"vae_19dec_fp16_tuned",
+    "stablediffusion/v1_4/vae/fp16/length_77/tuned/cuda":"vae_19dec_fp16_cuda_tuned",
+    "stablediffusion/v1_4/vae/fp16/length_77/untuned/base":"vae_8dec_fp16",
+    "stablediffusion/v1_4/vae/fp32/length_77/untuned":"vae_1_64_512_512_fp32_CompVis_stable_diffusion_v1_4",
+    "stablediffusion/v1_4/vae/fp32/length_64/untuned":"vae_1_64_512_512_fp32_CompVis_stable_diffusion_v1_4",
+    "stablediffusion/v1_4/clip/fp32/length_77/untuned":"clip_18dec_fp32",
+    "stablediffusion/v1_4/clip/fp32/length_64/untuned":"clip_1_64_512_512_fp32_CompVis_stable_diffusion_v1_4",
+    "stablediffusion/v2_1base/unet/fp16/length_77/untuned":"unet77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1base/unet/fp16/length_77/tuned":"unet2base_8dec_fp16_tuned_v2",
+    "stablediffusion/v2_1base/unet/fp16/length_77/tuned/cuda":"unet2base_8dec_fp16_cuda_tuned",
+    "stablediffusion/v2_1base/unet/fp16/length_64/untuned":"unet64_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1base/unet/fp16/length_64/tuned":"unet_19dec_v2p1base_fp16_64_tuned",
+    "stablediffusion/v2_1base/unet/fp16/length_64/tuned/cuda":"unet_19dec_v2p1base_fp16_64_cuda_tuned",
+    "stablediffusion/v2_1base/vae/fp16/length_77/untuned":"vae77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1base/vae/fp16/length_77/tuned":"vae2base_19dec_fp16_tuned",
+    "stablediffusion/v2_1base/vae/fp16/length_77/tuned/cuda":"vae2base_19dec_fp16_cuda_tuned",
+    "stablediffusion/v2_1base/vae/fp16/length_77/untuned/base":"vae2base_8dec_fp16",
+    "stablediffusion/v2_1base/vae/fp16/length_77/tuned/base":"vae2base_8dec_fp16_tuned",
+    "stablediffusion/v2_1base/vae/fp16/length_77/tuned/base/cuda":"vae2base_8dec_fp16_cuda_tuned",
+    "stablediffusion/v2_1base/clip/fp32/length_77/untuned":"clip77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1base/clip/fp32/length_64/untuned":"clip64_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1/unet/fp16/length_77/untuned":"unet77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1/vae/fp16/length_77/untuned":"vae77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1/vae/fp16/length_77/untuned/base":"vae2_8dec_fp16",
+    "stablediffusion/v2_1/clip/fp32/length_77/untuned":"clip77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "anythingv3/v1_4/unet/fp16/length_77/untuned":"av3_unet_19dec_fp16",
+    "anythingv3/v1_4/unet/fp16/length_77/tuned":"av3_unet_19dec_fp16_tuned",
+    "anythingv3/v1_4/unet/fp16/length_77/tuned/cuda":"av3_unet_19dec_fp16_cuda_tuned",
+    "anythingv3/v1_4/unet/fp32/length_77/untuned":"av3_unet_19dec_fp32",
+    "anythingv3/v1_4/vae/fp16/length_77/untuned":"av3_vae_19dec_fp16",
+    "anythingv3/v1_4/vae/fp16/length_77/tuned":"av3_vae_19dec_fp16_tuned",
+    "anythingv3/v1_4/vae/fp16/length_77/tuned/cuda":"av3_vae_19dec_fp16_cuda_tuned",
+    "anythingv3/v1_4/vae/fp16/length_77/untuned/base":"av3_vaebase_22dec_fp16",
+    "anythingv3/v1_4/vae/fp32/length_77/untuned":"av3_vae_19dec_fp32",
+    "anythingv3/v1_4/vae/fp32/length_77/untuned/base":"av3_vaebase_22dec_fp32",
+    "anythingv3/v1_4/clip/fp32/length_77/untuned":"av3_clip_19dec_fp32",
+    "analogdiffusion/v1_4/unet/fp16/length_77/untuned":"ad_unet_19dec_fp16",
+    "analogdiffusion/v1_4/unet/fp16/length_77/tuned":"ad_unet_19dec_fp16_tuned",
+    "analogdiffusion/v1_4/unet/fp16/length_77/tuned/cuda":"ad_unet_19dec_fp16_cuda_tuned",
+    "analogdiffusion/v1_4/unet/fp32/length_77/untuned":"ad_unet_19dec_fp32",
+    "analogdiffusion/v1_4/vae/fp16/length_77/untuned":"ad_vae_19dec_fp16",
+    "analogdiffusion/v1_4/vae/fp16/length_77/tuned":"ad_vae_19dec_fp16_tuned",
+    "analogdiffusion/v1_4/vae/fp16/length_77/tuned/cuda":"ad_vae_19dec_fp16_cuda_tuned",
+    "analogdiffusion/v1_4/vae/fp16/length_77/untuned/base":"ad_vaebase_22dec_fp16",
+    "analogdiffusion/v1_4/vae/fp32/length_77/untuned":"ad_vae_19dec_fp32",
+    "analogdiffusion/v1_4/vae/fp32/length_77/untuned/base":"ad_vaebase_22dec_fp32",
+    "analogdiffusion/v1_4/clip/fp32/length_77/untuned":"ad_clip_19dec_fp32",
+    "openjourney/v1_4/unet/fp16/length_64/untuned":"oj_unet_22dec_fp16_64",
+    "openjourney/v1_4/unet/fp32/length_64/untuned":"oj_unet_22dec_fp32_64",
+    "openjourney/v1_4/vae/fp16/length_77/untuned":"oj_vae_22dec_fp16",
+    "openjourney/v1_4/vae/fp16/length_77/untuned/base":"oj_vaebase_22dec_fp16",
+    "openjourney/v1_4/vae/fp32/length_77/untuned":"oj_vae_22dec_fp32",
+    "openjourney/v1_4/vae/fp32/length_77/untuned/base":"oj_vaebase_22dec_fp32",
+    "openjourney/v1_4/clip/fp32/length_64/untuned":"oj_clip_22dec_fp32_64",
+    "dreamlike/v1_4/unet/fp16/length_77/untuned":"dl_unet_23dec_fp16_77",
+    "dreamlike/v1_4/unet/fp32/length_77/untuned":"dl_unet_23dec_fp32_77",
+    "dreamlike/v1_4/vae/fp16/length_77/untuned":"dl_vae_23dec_fp16",
+    "dreamlike/v1_4/vae/fp16/length_77/untuned/base":"dl_vaebase_23dec_fp16",
+    "dreamlike/v1_4/vae/fp32/length_77/untuned":"dl_vae_23dec_fp32",
+    "dreamlike/v1_4/vae/fp32/length_77/untuned/base":"dl_vaebase_23dec_fp32",
+    "dreamlike/v1_4/clip/fp32/length_77/untuned":"dl_clip_23dec_fp32_77"
+  }
+]

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

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

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

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

apps/stable_diffusion/src/utils/sd_annotation.py

@@ -0,0 +1,244 @@
+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,
+    )
+
+    if args.ckpt_loc != "":
+        base_model_id = fetch_and_update_base_model_id(args.ckpt_loc)
+    else:
+        base_model_id = fetch_and_update_base_model_id(args.hf_model_id)
+        if base_model_id == "":
+            base_model_id = args.hf_model_id
+
+    variant, version = get_variant_version(base_model_id)
+
+    if version == "inpaint_v1":
+        version = "v1_4"
+    elif version == "inpaint_v2":
+        version = "v2_1base"
+
+    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

@@ -0,0 +1,520 @@
+import argparse
+import os
+from pathlib import Path
+
+
+def path_expand(s):
+    return Path(s).expanduser().resolve()
+
+
+def is_valid_file(arg):
+    if not os.path.exists(arg):
+        return None
+    else:
+        return arg
+
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+##############################################################################
+### Stable Diffusion Params
+##############################################################################
+
+p.add_argument(
+    "-p",
+    "--prompts",
+    nargs="+",
+    default=["cyberpunk forest by Salvador Dali"],
+    help="text of which images to be generated.",
+)
+
+p.add_argument(
+    "--negative_prompts",
+    nargs="+",
+    default=["trees, green"],
+    help="text you don't want to see in the generated image.",
+)
+
+p.add_argument(
+    "--img_path",
+    type=str,
+    help="Path to the image input for img2img/inpainting",
+)
+
+p.add_argument(
+    "--steps",
+    type=int,
+    default=50,
+    help="the no. of steps to do the sampling.",
+)
+
+p.add_argument(
+    "--seed",
+    type=int,
+    default=-1,
+    help="the seed to use. -1 for a random one.",
+)
+
+p.add_argument(
+    "--batch_size",
+    type=int,
+    default=1,
+    choices=range(1, 4),
+    help="the number of inferences to be made in a single `batch_count`.",
+)
+
+p.add_argument(
+    "--height",
+    type=int,
+    default=512,
+    choices=range(128, 769, 8),
+    help="the height of the output image.",
+)
+
+p.add_argument(
+    "--width",
+    type=int,
+    default=512,
+    choices=range(128, 769, 8),
+    help="the width of the output image.",
+)
+
+p.add_argument(
+    "--guidance_scale",
+    type=float,
+    default=7.5,
+    help="the value to be used for guidance scaling.",
+)
+
+p.add_argument(
+    "--noise_level",
+    type=int,
+    default=20,
+    help="the value to be used for noise level of upscaler.",
+)
+
+p.add_argument(
+    "--max_length",
+    type=int,
+    default=64,
+    help="max length of the tokenizer output, options are 64 and 77.",
+)
+
+p.add_argument(
+    "--strength",
+    type=float,
+    default=0.8,
+    help="the strength of change applied on the given input image for img2img",
+)
+
+##############################################################################
+### Stable Diffusion Training Params
+##############################################################################
+
+p.add_argument(
+    "--lora_save_dir",
+    type=str,
+    default="models/lora/",
+    help="Directory to save the lora fine tuned model",
+)
+
+p.add_argument(
+    "--training_images_dir",
+    type=str,
+    default="models/lora/training_images/",
+    help="Directory containing images that are an example of the prompt",
+)
+
+p.add_argument(
+    "--training_steps",
+    type=int,
+    default=2000,
+    help="The no. of steps to train",
+)
+
+##############################################################################
+### Inpainting and Outpainting Params
+##############################################################################
+
+p.add_argument(
+    "--mask_path",
+    type=str,
+    help="Path to the mask image input for inpainting",
+)
+
+p.add_argument(
+    "--inpaint_full_res",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If inpaint only masked area or whole picture",
+)
+
+p.add_argument(
+    "--inpaint_full_res_padding",
+    type=int,
+    default=32,
+    choices=range(0, 257, 4),
+    help="Number of pixels for only masked padding",
+)
+
+p.add_argument(
+    "--pixels",
+    type=int,
+    default=128,
+    choices=range(8, 257, 8),
+    help="Number of expended pixels for one direction for outpainting",
+)
+
+p.add_argument(
+    "--mask_blur",
+    type=int,
+    default=8,
+    choices=range(0, 65),
+    help="Number of blur pixels for outpainting",
+)
+
+p.add_argument(
+    "--left",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="If 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
+##############################################################################
+
+p.add_argument(
+    "--device", type=str, default="vulkan", help="device to run the model."
+)
+
+p.add_argument(
+    "--precision", type=str, default="fp16", help="precision to run the model."
+)
+
+p.add_argument(
+    "--import_mlir",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="imports the model from torch module to shark_module otherwise downloads the model from shark_tank.",
+)
+
+p.add_argument(
+    "--load_vmfb",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="attempts to load the model from a precompiled flatbuffer and compiles + saves it if not found.",
+)
+
+p.add_argument(
+    "--save_vmfb",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="saves the compiled flatbuffer to the local directory",
+)
+
+p.add_argument(
+    "--use_tuned",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="Download and use the tuned version of the model if available",
+)
+
+p.add_argument(
+    "--use_base_vae",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Do conversion from the VAE output to pixel space on cpu.",
+)
+
+p.add_argument(
+    "--scheduler",
+    type=str,
+    default="SharkEulerDiscrete",
+    help="other supported schedulers are [PNDM, DDIM, LMSDiscrete, EulerDiscrete, DPMSolverMultistep]",
+)
+
+p.add_argument(
+    "--output_img_format",
+    type=str,
+    default="png",
+    help="specify the format in which output image is save. Supported options: jpg / png",
+)
+
+p.add_argument(
+    "--output_dir",
+    type=str,
+    default=None,
+    help="Directory path to save the output images and json",
+)
+
+p.add_argument(
+    "--batch_count",
+    type=int,
+    default=1,
+    help="number of batch to be generated with random seeds in single execution",
+)
+
+p.add_argument(
+    "--ckpt_loc",
+    type=str,
+    default="",
+    help="Path to SD's .ckpt file.",
+)
+
+p.add_argument(
+    "--custom_vae",
+    type=str,
+    default="",
+    help="HuggingFace repo-id or path to SD model's checkpoint whose Vae needs to be plugged in.",
+)
+
+p.add_argument(
+    "--hf_model_id",
+    type=str,
+    default="stabilityai/stable-diffusion-2-1-base",
+    help="The repo-id of hugging face.",
+)
+
+p.add_argument(
+    "--low_cpu_mem_usage",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Use the accelerate package to reduce cpu memory consumption",
+)
+
+p.add_argument(
+    "--attention_slicing",
+    type=str,
+    default="none",
+    help="Amount of attention slicing to use (one of 'max', 'auto', 'none', or an integer)",
+)
+
+p.add_argument(
+    "--use_stencil",
+    choices=["canny", "openpose", "scribble"],
+    help="Enable the stencil feature.",
+)
+
+p.add_argument(
+    "--use_lora",
+    type=str,
+    default="",
+    help="Use standalone LoRA weight using a HF ID or a checkpoint file (~3 MB)",
+)
+
+##############################################################################
+### IREE - Vulkan supported flags
+##############################################################################
+
+p.add_argument(
+    "--iree_vulkan_target_triple",
+    type=str,
+    default="",
+    help="Specify target triple for vulkan",
+)
+
+p.add_argument(
+    "--vulkan_debug_utils",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Profiles vulkan device and collects the .rdc info",
+)
+
+p.add_argument(
+    "--vulkan_large_heap_block_size",
+    default="4147483648",
+    help="flag for setting VMA preferredLargeHeapBlockSize for vulkan device, default is 4G",
+)
+
+p.add_argument(
+    "--vulkan_validation_layers",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for disabling vulkan validation layers when benchmarking",
+)
+
+##############################################################################
+### Misc. Debug and Optimization flags
+##############################################################################
+
+p.add_argument(
+    "--use_compiled_scheduler",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="use the default scheduler precompiled into the model if available",
+)
+
+p.add_argument(
+    "--local_tank_cache",
+    default="",
+    help="Specify where to save downloaded shark_tank artifacts. If this is not set, the default is ~/.local/shark_tank/.",
+)
+
+p.add_argument(
+    "--dump_isa",
+    default=False,
+    action="store_true",
+    help="When enabled call amdllpc to get ISA dumps. use with dispatch benchmarks.",
+)
+
+p.add_argument(
+    "--dispatch_benchmarks",
+    default=None,
+    help='dispatches to return benchamrk data on.  use "All" for all, and None for none.',
+)
+
+p.add_argument(
+    "--dispatch_benchmarks_dir",
+    default="temp_dispatch_benchmarks",
+    help='directory where you want to store dispatch data generated with "--dispatch_benchmarks"',
+)
+
+p.add_argument(
+    "--enable_rgp",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for inserting debug frames between iterations for use with rgp.",
+)
+
+p.add_argument(
+    "--hide_steps",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="flag for hiding the details of iteration/sec for each step.",
+)
+
+p.add_argument(
+    "--warmup_count",
+    type=int,
+    default=0,
+    help="flag setting warmup count for clip and vae [>= 0].",
+)
+
+p.add_argument(
+    "--clear_all",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag to clear all mlir and vmfb from common locations. Recompiling will take several minutes",
+)
+
+p.add_argument(
+    "--save_metadata_to_json",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for whether or not to save a generation information json file with the image.",
+)
+
+p.add_argument(
+    "--write_metadata_to_png",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="flag for whether or not to save generation information in PNG chunk text to generated images.",
+)
+
+p.add_argument(
+    "--import_debug",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="if import_mlir is True, saves mlir via the debug option in shark importer. Does nothing if import_mlir is false (the default)",
+)
+##############################################################################
+### Web UI flags
+##############################################################################
+
+p.add_argument(
+    "--progress_bar",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="flag for removing the progress bar animation during image generation",
+)
+
+p.add_argument(
+    "--ckpt_dir",
+    type=str,
+    default="",
+    help="Path to directory where all .ckpts are stored in order to populate them in the web UI",
+)
+
+
+p.add_argument(
+    "--share",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for generating a public URL",
+)
+
+p.add_argument(
+    "--server_port",
+    type=int,
+    default=8080,
+    help="flag for setting server port",
+)
+
+##############################################################################
+### SD model auto-annotation flags
+##############################################################################
+
+p.add_argument(
+    "--annotation_output",
+    type=path_expand,
+    default="./",
+    help="Directory to save the annotated mlir file",
+)
+
+p.add_argument(
+    "--annotation_model",
+    type=str,
+    default="unet",
+    help="Options are unet and vae.",
+)
+
+p.add_argument(
+    "--save_annotation",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Save annotated mlir file",
+)
+
+args, unknown = p.parse_known_args()
+if args.import_debug:
+    os.environ["IREE_SAVE_TEMPS"] = os.path.join(
+        os.getcwd(), args.hf_model_id.replace("/", "_")
+    )

apps/stable_diffusion/src/utils/stencils/__init__.py

@@ -0,0 +1,2 @@
+from apps.stable_diffusion.src.utils.stencils.canny import CannyDetector
+from apps.stable_diffusion.src.utils.stencils.openpose import OpenposeDetector

apps/stable_diffusion/src/utils/stencils/canny/__init__.py

@@ -0,0 +1,6 @@
+import cv2
+
+
+class CannyDetector:
+    def __call__(self, img, low_threshold, high_threshold):
+        return cv2.Canny(img, low_threshold, high_threshold)

apps/stable_diffusion/src/utils/stencils/openpose/__init__.py

@@ -0,0 +1,62 @@
+import requests
+from pathlib import Path
+
+import torch
+import numpy as np
+
+# from annotator.util import annotator_ckpts_path
+from apps.stable_diffusion.src.utils.stencils.openpose.body import Body
+from apps.stable_diffusion.src.utils.stencils.openpose.hand import Hand
+from apps.stable_diffusion.src.utils.stencils.openpose.openpose_util import (
+    draw_bodypose,
+    draw_handpose,
+    handDetect,
+)
+
+
+body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
+hand_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/hand_pose_model.pth"
+
+
+class OpenposeDetector:
+    def __init__(self):
+        cwd = Path.cwd()
+        ckpt_path = Path(cwd, "stencil_annotator")
+        ckpt_path.mkdir(parents=True, exist_ok=True)
+        body_modelpath = ckpt_path / "body_pose_model.pth"
+        hand_modelpath = ckpt_path / "hand_pose_model.pth"
+
+        if not body_modelpath.is_file():
+            r = requests.get(body_model_path, allow_redirects=True)
+            open(body_modelpath, "wb").write(r.content)
+        if not hand_modelpath.is_file():
+            r = requests.get(hand_model_path, allow_redirects=True)
+            open(hand_modelpath, "wb").write(r.content)
+
+        self.body_estimation = Body(body_modelpath)
+        self.hand_estimation = Hand(hand_modelpath)
+
+    def __call__(self, oriImg, hand=False):
+        oriImg = oriImg[:, :, ::-1].copy()
+        with torch.no_grad():
+            candidate, subset = self.body_estimation(oriImg)
+            canvas = np.zeros_like(oriImg)
+            canvas = draw_bodypose(canvas, candidate, subset)
+            if hand:
+                hands_list = handDetect(candidate, subset, oriImg)
+                all_hand_peaks = []
+                for x, y, w, is_left in hands_list:
+                    peaks = self.hand_estimation(
+                        oriImg[y : y + w, x : x + w, :]
+                    )
+                    peaks[:, 0] = np.where(
+                        peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x
+                    )
+                    peaks[:, 1] = np.where(
+                        peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y
+                    )
+                    all_hand_peaks.append(peaks)
+                canvas = draw_handpose(canvas, all_hand_peaks)
+            return canvas, dict(
+                candidate=candidate.tolist(), subset=subset.tolist()
+            )

apps/stable_diffusion/src/utils/stencils/openpose/body.py

@@ -0,0 +1,499 @@
+import cv2
+import numpy as np
+import math
+from scipy.ndimage.filters import gaussian_filter
+import torch
+import torch.nn as nn
+from collections import OrderedDict
+from apps.stable_diffusion.src.utils.stencils.openpose.openpose_util import (
+    make_layers,
+    transfer,
+    padRightDownCorner,
+)
+
+
+class BodyPoseModel(nn.Module):
+    def __init__(self):
+        super(BodyPoseModel, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = [
+            "conv5_5_CPM_L1",
+            "conv5_5_CPM_L2",
+            "Mconv7_stage2_L1",
+            "Mconv7_stage2_L2",
+            "Mconv7_stage3_L1",
+            "Mconv7_stage3_L2",
+            "Mconv7_stage4_L1",
+            "Mconv7_stage4_L2",
+            "Mconv7_stage5_L1",
+            "Mconv7_stage5_L2",
+            "Mconv7_stage6_L1",
+            "Mconv7_stage6_L1",
+        ]
+        blocks = {}
+        block0 = OrderedDict(
+            [
+                ("conv1_1", [3, 64, 3, 1, 1]),
+                ("conv1_2", [64, 64, 3, 1, 1]),
+                ("pool1_stage1", [2, 2, 0]),
+                ("conv2_1", [64, 128, 3, 1, 1]),
+                ("conv2_2", [128, 128, 3, 1, 1]),
+                ("pool2_stage1", [2, 2, 0]),
+                ("conv3_1", [128, 256, 3, 1, 1]),
+                ("conv3_2", [256, 256, 3, 1, 1]),
+                ("conv3_3", [256, 256, 3, 1, 1]),
+                ("conv3_4", [256, 256, 3, 1, 1]),
+                ("pool3_stage1", [2, 2, 0]),
+                ("conv4_1", [256, 512, 3, 1, 1]),
+                ("conv4_2", [512, 512, 3, 1, 1]),
+                ("conv4_3_CPM", [512, 256, 3, 1, 1]),
+                ("conv4_4_CPM", [256, 128, 3, 1, 1]),
+            ]
+        )
+
+        # Stage 1
+        block1_1 = OrderedDict(
+            [
+                ("conv5_1_CPM_L1", [128, 128, 3, 1, 1]),
+                ("conv5_2_CPM_L1", [128, 128, 3, 1, 1]),
+                ("conv5_3_CPM_L1", [128, 128, 3, 1, 1]),
+                ("conv5_4_CPM_L1", [128, 512, 1, 1, 0]),
+                ("conv5_5_CPM_L1", [512, 38, 1, 1, 0]),
+            ]
+        )
+
+        block1_2 = OrderedDict(
+            [
+                ("conv5_1_CPM_L2", [128, 128, 3, 1, 1]),
+                ("conv5_2_CPM_L2", [128, 128, 3, 1, 1]),
+                ("conv5_3_CPM_L2", [128, 128, 3, 1, 1]),
+                ("conv5_4_CPM_L2", [128, 512, 1, 1, 0]),
+                ("conv5_5_CPM_L2", [512, 19, 1, 1, 0]),
+            ]
+        )
+        blocks["block1_1"] = block1_1
+        blocks["block1_2"] = block1_2
+
+        self.model0 = make_layers(block0, no_relu_layers)
+
+        # Stages 2 - 6
+        for i in range(2, 7):
+            blocks["block%d_1" % i] = OrderedDict(
+                [
+                    ("Mconv1_stage%d_L1" % i, [185, 128, 7, 1, 3]),
+                    ("Mconv2_stage%d_L1" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv3_stage%d_L1" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv4_stage%d_L1" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv5_stage%d_L1" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv6_stage%d_L1" % i, [128, 128, 1, 1, 0]),
+                    ("Mconv7_stage%d_L1" % i, [128, 38, 1, 1, 0]),
+                ]
+            )
+
+            blocks["block%d_2" % i] = OrderedDict(
+                [
+                    ("Mconv1_stage%d_L2" % i, [185, 128, 7, 1, 3]),
+                    ("Mconv2_stage%d_L2" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv3_stage%d_L2" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv4_stage%d_L2" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv5_stage%d_L2" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv6_stage%d_L2" % i, [128, 128, 1, 1, 0]),
+                    ("Mconv7_stage%d_L2" % i, [128, 19, 1, 1, 0]),
+                ]
+            )
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_1 = blocks["block1_1"]
+        self.model2_1 = blocks["block2_1"]
+        self.model3_1 = blocks["block3_1"]
+        self.model4_1 = blocks["block4_1"]
+        self.model5_1 = blocks["block5_1"]
+        self.model6_1 = blocks["block6_1"]
+
+        self.model1_2 = blocks["block1_2"]
+        self.model2_2 = blocks["block2_2"]
+        self.model3_2 = blocks["block3_2"]
+        self.model4_2 = blocks["block4_2"]
+        self.model5_2 = blocks["block5_2"]
+        self.model6_2 = blocks["block6_2"]
+
+    def forward(self, x):
+        out1 = self.model0(x)
+
+        out1_1 = self.model1_1(out1)
+        out1_2 = self.model1_2(out1)
+        out2 = torch.cat([out1_1, out1_2, out1], 1)
+
+        out2_1 = self.model2_1(out2)
+        out2_2 = self.model2_2(out2)
+        out3 = torch.cat([out2_1, out2_2, out1], 1)
+
+        out3_1 = self.model3_1(out3)
+        out3_2 = self.model3_2(out3)
+        out4 = torch.cat([out3_1, out3_2, out1], 1)
+
+        out4_1 = self.model4_1(out4)
+        out4_2 = self.model4_2(out4)
+        out5 = torch.cat([out4_1, out4_2, out1], 1)
+
+        out5_1 = self.model5_1(out5)
+        out5_2 = self.model5_2(out5)
+        out6 = torch.cat([out5_1, out5_2, out1], 1)
+
+        out6_1 = self.model6_1(out6)
+        out6_2 = self.model6_2(out6)
+
+        return out6_1, out6_2
+
+
+class Body(object):
+    def __init__(self, model_path):
+        self.model = BodyPoseModel()
+        if torch.cuda.is_available():
+            self.model = self.model.cuda()
+        model_dict = transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def __call__(self, oriImg):
+        scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre1 = 0.1
+        thre2 = 0.05
+        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
+        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
+        paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = cv2.resize(
+                oriImg,
+                (0, 0),
+                fx=scale,
+                fy=scale,
+                interpolation=cv2.INTER_CUBIC,
+            )
+            imageToTest_padded, pad = padRightDownCorner(
+                imageToTest, stride, padValue
+            )
+            im = (
+                np.transpose(
+                    np.float32(imageToTest_padded[:, :, :, np.newaxis]),
+                    (3, 2, 0, 1),
+                )
+                / 256
+                - 0.5
+            )
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            if torch.cuda.is_available():
+                data = data.cuda()
+            with torch.no_grad():
+                Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
+            Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
+            Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
+
+            # extract outputs, resize, and remove padding
+            heatmap = np.transpose(
+                np.squeeze(Mconv7_stage6_L2), (1, 2, 0)
+            )  # output 1 is heatmaps
+            heatmap = cv2.resize(
+                heatmap,
+                (0, 0),
+                fx=stride,
+                fy=stride,
+                interpolation=cv2.INTER_CUBIC,
+            )
+            heatmap = heatmap[
+                : imageToTest_padded.shape[0] - pad[2],
+                : imageToTest_padded.shape[1] - pad[3],
+                :,
+            ]
+            heatmap = cv2.resize(
+                heatmap,
+                (oriImg.shape[1], oriImg.shape[0]),
+                interpolation=cv2.INTER_CUBIC,
+            )
+
+            # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
+            paf = np.transpose(
+                np.squeeze(Mconv7_stage6_L1), (1, 2, 0)
+            )  # output 0 is PAFs
+            paf = cv2.resize(
+                paf,
+                (0, 0),
+                fx=stride,
+                fy=stride,
+                interpolation=cv2.INTER_CUBIC,
+            )
+            paf = paf[
+                : imageToTest_padded.shape[0] - pad[2],
+                : imageToTest_padded.shape[1] - pad[3],
+                :,
+            ]
+            paf = cv2.resize(
+                paf,
+                (oriImg.shape[1], oriImg.shape[0]),
+                interpolation=cv2.INTER_CUBIC,
+            )
+
+            heatmap_avg += heatmap_avg + heatmap / len(multiplier)
+            paf_avg += +paf / len(multiplier)
+
+        all_peaks = []
+        peak_counter = 0
+
+        for part in range(18):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+
+            map_left = np.zeros(one_heatmap.shape)
+            map_left[1:, :] = one_heatmap[:-1, :]
+            map_right = np.zeros(one_heatmap.shape)
+            map_right[:-1, :] = one_heatmap[1:, :]
+            map_up = np.zeros(one_heatmap.shape)
+            map_up[:, 1:] = one_heatmap[:, :-1]
+            map_down = np.zeros(one_heatmap.shape)
+            map_down[:, :-1] = one_heatmap[:, 1:]
+
+            peaks_binary = np.logical_and.reduce(
+                (
+                    one_heatmap >= map_left,
+                    one_heatmap >= map_right,
+                    one_heatmap >= map_up,
+                    one_heatmap >= map_down,
+                    one_heatmap > thre1,
+                )
+            )
+            peaks = list(
+                zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])
+            )  # note reverse
+            peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
+            peak_id = range(peak_counter, peak_counter + len(peaks))
+            peaks_with_score_and_id = [
+                peaks_with_score[i] + (peak_id[i],)
+                for i in range(len(peak_id))
+            ]
+
+            all_peaks.append(peaks_with_score_and_id)
+            peak_counter += len(peaks)
+
+        # find connection in the specified sequence, center 29 is in the position 15
+        limbSeq = [
+            [2, 3],
+            [2, 6],
+            [3, 4],
+            [4, 5],
+            [6, 7],
+            [7, 8],
+            [2, 9],
+            [9, 10],
+            [10, 11],
+            [2, 12],
+            [12, 13],
+            [13, 14],
+            [2, 1],
+            [1, 15],
+            [15, 17],
+            [1, 16],
+            [16, 18],
+            [3, 17],
+            [6, 18],
+        ]
+        # the middle joints heatmap correpondence
+        mapIdx = [
+            [31, 32],
+            [39, 40],
+            [33, 34],
+            [35, 36],
+            [41, 42],
+            [43, 44],
+            [19, 20],
+            [21, 22],
+            [23, 24],
+            [25, 26],
+            [27, 28],
+            [29, 30],
+            [47, 48],
+            [49, 50],
+            [53, 54],
+            [51, 52],
+            [55, 56],
+            [37, 38],
+            [45, 46],
+        ]
+
+        connection_all = []
+        special_k = []
+        mid_num = 10
+
+        for k in range(len(mapIdx)):
+            score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
+            candA = all_peaks[limbSeq[k][0] - 1]
+            candB = all_peaks[limbSeq[k][1] - 1]
+            nA = len(candA)
+            nB = len(candB)
+            indexA, indexB = limbSeq[k]
+            if nA != 0 and nB != 0:
+                connection_candidate = []
+                for i in range(nA):
+                    for j in range(nB):
+                        vec = np.subtract(candB[j][:2], candA[i][:2])
+                        norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
+                        norm = max(0.001, norm)
+                        vec = np.divide(vec, norm)
+
+                        startend = list(
+                            zip(
+                                np.linspace(
+                                    candA[i][0], candB[j][0], num=mid_num
+                                ),
+                                np.linspace(
+                                    candA[i][1], candB[j][1], num=mid_num
+                                ),
+                            )
+                        )
+
+                        vec_x = np.array(
+                            [
+                                score_mid[
+                                    int(round(startend[I][1])),
+                                    int(round(startend[I][0])),
+                                    0,
+                                ]
+                                for I in range(len(startend))
+                            ]
+                        )
+                        vec_y = np.array(
+                            [
+                                score_mid[
+                                    int(round(startend[I][1])),
+                                    int(round(startend[I][0])),
+                                    1,
+                                ]
+                                for I in range(len(startend))
+                            ]
+                        )
+
+                        score_midpts = np.multiply(
+                            vec_x, vec[0]
+                        ) + np.multiply(vec_y, vec[1])
+                        score_with_dist_prior = sum(score_midpts) / len(
+                            score_midpts
+                        ) + min(0.5 * oriImg.shape[0] / norm - 1, 0)
+                        criterion1 = len(
+                            np.nonzero(score_midpts > thre2)[0]
+                        ) > 0.8 * len(score_midpts)
+                        criterion2 = score_with_dist_prior > 0
+                        if criterion1 and criterion2:
+                            connection_candidate.append(
+                                [
+                                    i,
+                                    j,
+                                    score_with_dist_prior,
+                                    score_with_dist_prior
+                                    + candA[i][2]
+                                    + candB[j][2],
+                                ]
+                            )
+
+                connection_candidate = sorted(
+                    connection_candidate, key=lambda x: x[2], reverse=True
+                )
+                connection = np.zeros((0, 5))
+                for c in range(len(connection_candidate)):
+                    i, j, s = connection_candidate[c][0:3]
+                    if i not in connection[:, 3] and j not in connection[:, 4]:
+                        connection = np.vstack(
+                            [connection, [candA[i][3], candB[j][3], s, i, j]]
+                        )
+                        if len(connection) >= min(nA, nB):
+                            break
+
+                connection_all.append(connection)
+            else:
+                special_k.append(k)
+                connection_all.append([])
+
+        # last number in each row is the total parts number of that person
+        # the second last number in each row is the score of the overall configuration
+        subset = -1 * np.ones((0, 20))
+        candidate = np.array(
+            [item for sublist in all_peaks for item in sublist]
+        )
+
+        for k in range(len(mapIdx)):
+            if k not in special_k:
+                partAs = connection_all[k][:, 0]
+                partBs = connection_all[k][:, 1]
+                indexA, indexB = np.array(limbSeq[k]) - 1
+
+                for i in range(len(connection_all[k])):  # = 1:size(temp,1)
+                    found = 0
+                    subset_idx = [-1, -1]
+                    for j in range(len(subset)):  # 1:size(subset,1):
+                        if (
+                            subset[j][indexA] == partAs[i]
+                            or subset[j][indexB] == partBs[i]
+                        ):
+                            subset_idx[found] = j
+                            found += 1
+
+                    if found == 1:
+                        j = subset_idx[0]
+                        if subset[j][indexB] != partBs[i]:
+                            subset[j][indexB] = partBs[i]
+                            subset[j][-1] += 1
+                            subset[j][-2] += (
+                                candidate[partBs[i].astype(int), 2]
+                                + connection_all[k][i][2]
+                            )
+                    elif found == 2:  # if found 2 and disjoint, merge them
+                        j1, j2 = subset_idx
+                        membership = (
+                            (subset[j1] >= 0).astype(int)
+                            + (subset[j2] >= 0).astype(int)
+                        )[:-2]
+                        if len(np.nonzero(membership == 2)[0]) == 0:  # merge
+                            subset[j1][:-2] += subset[j2][:-2] + 1
+                            subset[j1][-2:] += subset[j2][-2:]
+                            subset[j1][-2] += connection_all[k][i][2]
+                            subset = np.delete(subset, j2, 0)
+                        else:  # as like found == 1
+                            subset[j1][indexB] = partBs[i]
+                            subset[j1][-1] += 1
+                            subset[j1][-2] += (
+                                candidate[partBs[i].astype(int), 2]
+                                + connection_all[k][i][2]
+                            )
+
+                    # if find no partA in the subset, create a new subset
+                    elif not found and k < 17:
+                        row = -1 * np.ones(20)
+                        row[indexA] = partAs[i]
+                        row[indexB] = partBs[i]
+                        row[-1] = 2
+                        row[-2] = (
+                            sum(
+                                candidate[
+                                    connection_all[k][i, :2].astype(int), 2
+                                ]
+                            )
+                            + connection_all[k][i][2]
+                        )
+                        subset = np.vstack([subset, row])
+        # delete some rows of subset which has few parts occur
+        deleteIdx = []
+        for i in range(len(subset)):
+            if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
+                deleteIdx.append(i)
+        subset = np.delete(subset, deleteIdx, axis=0)
+
+        # candidate: x, y, score, id
+        return candidate, subset

apps/stable_diffusion/src/utils/stencils/openpose/hand.py

@@ -0,0 +1,205 @@
+import cv2
+import numpy as np
+from scipy.ndimage.filters import gaussian_filter
+import torch
+import torch.nn as nn
+from skimage.measure import label
+from collections import OrderedDict
+from apps.stable_diffusion.src.utils.stencils.openpose.openpose_util import (
+    make_layers,
+    transfer,
+    padRightDownCorner,
+    npmax,
+)
+
+
+class HandPoseModel(nn.Module):
+    def __init__(self):
+        super(HandPoseModel, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = [
+            "conv6_2_CPM",
+            "Mconv7_stage2",
+            "Mconv7_stage3",
+            "Mconv7_stage4",
+            "Mconv7_stage5",
+            "Mconv7_stage6",
+        ]
+        # stage 1
+        block1_0 = OrderedDict(
+            [
+                ("conv1_1", [3, 64, 3, 1, 1]),
+                ("conv1_2", [64, 64, 3, 1, 1]),
+                ("pool1_stage1", [2, 2, 0]),
+                ("conv2_1", [64, 128, 3, 1, 1]),
+                ("conv2_2", [128, 128, 3, 1, 1]),
+                ("pool2_stage1", [2, 2, 0]),
+                ("conv3_1", [128, 256, 3, 1, 1]),
+                ("conv3_2", [256, 256, 3, 1, 1]),
+                ("conv3_3", [256, 256, 3, 1, 1]),
+                ("conv3_4", [256, 256, 3, 1, 1]),
+                ("pool3_stage1", [2, 2, 0]),
+                ("conv4_1", [256, 512, 3, 1, 1]),
+                ("conv4_2", [512, 512, 3, 1, 1]),
+                ("conv4_3", [512, 512, 3, 1, 1]),
+                ("conv4_4", [512, 512, 3, 1, 1]),
+                ("conv5_1", [512, 512, 3, 1, 1]),
+                ("conv5_2", [512, 512, 3, 1, 1]),
+                ("conv5_3_CPM", [512, 128, 3, 1, 1]),
+            ]
+        )
+
+        block1_1 = OrderedDict(
+            [
+                ("conv6_1_CPM", [128, 512, 1, 1, 0]),
+                ("conv6_2_CPM", [512, 22, 1, 1, 0]),
+            ]
+        )
+
+        blocks = {}
+        blocks["block1_0"] = block1_0
+        blocks["block1_1"] = block1_1
+
+        # stage 2-6
+        for i in range(2, 7):
+            blocks["block%d" % i] = OrderedDict(
+                [
+                    ("Mconv1_stage%d" % i, [150, 128, 7, 1, 3]),
+                    ("Mconv2_stage%d" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv3_stage%d" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv4_stage%d" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv5_stage%d" % i, [128, 128, 7, 1, 3]),
+                    ("Mconv6_stage%d" % i, [128, 128, 1, 1, 0]),
+                    ("Mconv7_stage%d" % i, [128, 22, 1, 1, 0]),
+                ]
+            )
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_0 = blocks["block1_0"]
+        self.model1_1 = blocks["block1_1"]
+        self.model2 = blocks["block2"]
+        self.model3 = blocks["block3"]
+        self.model4 = blocks["block4"]
+        self.model5 = blocks["block5"]
+        self.model6 = blocks["block6"]
+
+    def forward(self, x):
+        out1_0 = self.model1_0(x)
+        out1_1 = self.model1_1(out1_0)
+        concat_stage2 = torch.cat([out1_1, out1_0], 1)
+        out_stage2 = self.model2(concat_stage2)
+        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
+        out_stage3 = self.model3(concat_stage3)
+        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
+        out_stage4 = self.model4(concat_stage4)
+        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
+        out_stage5 = self.model5(concat_stage5)
+        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
+        out_stage6 = self.model6(concat_stage6)
+        return out_stage6
+
+
+class Hand(object):
+    def __init__(self, model_path):
+        self.model = HandPoseModel()
+        if torch.cuda.is_available():
+            self.model = self.model.cuda()
+        model_dict = transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def __call__(self, oriImg):
+        scale_search = [0.5, 1.0, 1.5, 2.0]
+        # scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre = 0.05
+        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
+        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 22))
+        # paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = cv2.resize(
+                oriImg,
+                (0, 0),
+                fx=scale,
+                fy=scale,
+                interpolation=cv2.INTER_CUBIC,
+            )
+            imageToTest_padded, pad = padRightDownCorner(
+                imageToTest, stride, padValue
+            )
+            im = (
+                np.transpose(
+                    np.float32(imageToTest_padded[:, :, :, np.newaxis]),
+                    (3, 2, 0, 1),
+                )
+                / 256
+                - 0.5
+            )
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            if torch.cuda.is_available():
+                data = data.cuda()
+            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
+            with torch.no_grad():
+                output = self.model(data).cpu().numpy()
+                # output = self.model(data).numpy()q
+
+            # extract outputs, resize, and remove padding
+            heatmap = np.transpose(
+                np.squeeze(output), (1, 2, 0)
+            )  # output 1 is heatmaps
+            heatmap = cv2.resize(
+                heatmap,
+                (0, 0),
+                fx=stride,
+                fy=stride,
+                interpolation=cv2.INTER_CUBIC,
+            )
+            heatmap = heatmap[
+                : imageToTest_padded.shape[0] - pad[2],
+                : imageToTest_padded.shape[1] - pad[3],
+                :,
+            ]
+            heatmap = cv2.resize(
+                heatmap,
+                (oriImg.shape[1], oriImg.shape[0]),
+                interpolation=cv2.INTER_CUBIC,
+            )
+
+            heatmap_avg += heatmap / len(multiplier)
+
+        all_peaks = []
+        for part in range(21):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+            binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
+            # 全部小于阈值
+            if np.sum(binary) == 0:
+                all_peaks.append([0, 0])
+                continue
+            label_img, label_numbers = label(
+                binary, return_num=True, connectivity=binary.ndim
+            )
+            max_index = (
+                np.argmax(
+                    [
+                        np.sum(map_ori[label_img == i])
+                        for i in range(1, label_numbers + 1)
+                    ]
+                )
+                + 1
+            )
+            label_img[label_img != max_index] = 0
+            map_ori[label_img == 0] = 0
+
+            y, x = npmax(map_ori)
+            all_peaks.append([x, y])
+        return np.array(all_peaks)

apps/stable_diffusion/src/utils/stencils/openpose/openpose_util.py

@@ -0,0 +1,272 @@
+import math
+import numpy as np
+import matplotlib
+import cv2
+from collections import OrderedDict
+import torch.nn as nn
+
+
+def make_layers(block, no_relu_layers):
+    layers = []
+    for layer_name, v in block.items():
+        if "pool" in layer_name:
+            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
+            layers.append((layer_name, layer))
+        else:
+            conv2d = nn.Conv2d(
+                in_channels=v[0],
+                out_channels=v[1],
+                kernel_size=v[2],
+                stride=v[3],
+                padding=v[4],
+            )
+            layers.append((layer_name, conv2d))
+            if layer_name not in no_relu_layers:
+                layers.append(("relu_" + layer_name, nn.ReLU(inplace=True)))
+
+    return nn.Sequential(OrderedDict(layers))
+
+
+def padRightDownCorner(img, stride, padValue):
+    h = img.shape[0]
+    w = img.shape[1]
+
+    pad = 4 * [None]
+    pad[0] = 0  # up
+    pad[1] = 0  # left
+    pad[2] = 0 if (h % stride == 0) else stride - (h % stride)  # down
+    pad[3] = 0 if (w % stride == 0) else stride - (w % stride)  # right
+
+    img_padded = img
+    pad_up = np.tile(img_padded[0:1, :, :] * 0 + padValue, (pad[0], 1, 1))
+    img_padded = np.concatenate((pad_up, img_padded), axis=0)
+    pad_left = np.tile(img_padded[:, 0:1, :] * 0 + padValue, (1, pad[1], 1))
+    img_padded = np.concatenate((pad_left, img_padded), axis=1)
+    pad_down = np.tile(img_padded[-2:-1, :, :] * 0 + padValue, (pad[2], 1, 1))
+    img_padded = np.concatenate((img_padded, pad_down), axis=0)
+    pad_right = np.tile(img_padded[:, -2:-1, :] * 0 + padValue, (1, pad[3], 1))
+    img_padded = np.concatenate((img_padded, pad_right), axis=1)
+
+    return img_padded, pad
+
+
+# transfer caffe model to pytorch which will match the layer name
+def transfer(model, model_weights):
+    transfered_model_weights = {}
+    for weights_name in model.state_dict().keys():
+        transfered_model_weights[weights_name] = model_weights[
+            ".".join(weights_name.split(".")[1:])
+        ]
+    return transfered_model_weights
+
+
+# draw the body keypoint and lims
+def draw_bodypose(canvas, candidate, subset):
+    stickwidth = 4
+    limbSeq = [
+        [2, 3],
+        [2, 6],
+        [3, 4],
+        [4, 5],
+        [6, 7],
+        [7, 8],
+        [2, 9],
+        [9, 10],
+        [10, 11],
+        [2, 12],
+        [12, 13],
+        [13, 14],
+        [2, 1],
+        [1, 15],
+        [15, 17],
+        [1, 16],
+        [16, 18],
+        [3, 17],
+        [6, 18],
+    ]
+
+    colors = [
+        [255, 0, 0],
+        [255, 85, 0],
+        [255, 170, 0],
+        [255, 255, 0],
+        [170, 255, 0],
+        [85, 255, 0],
+        [0, 255, 0],
+        [0, 255, 85],
+        [0, 255, 170],
+        [0, 255, 255],
+        [0, 170, 255],
+        [0, 85, 255],
+        [0, 0, 255],
+        [85, 0, 255],
+        [170, 0, 255],
+        [255, 0, 255],
+        [255, 0, 170],
+        [255, 0, 85],
+    ]
+    for i in range(18):
+        for n in range(len(subset)):
+            index = int(subset[n][i])
+            if index == -1:
+                continue
+            x, y = candidate[index][0:2]
+            cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
+    for i in range(17):
+        for n in range(len(subset)):
+            index = subset[n][np.array(limbSeq[i]) - 1]
+            if -1 in index:
+                continue
+            cur_canvas = canvas.copy()
+            Y = candidate[index.astype(int), 0]
+            X = candidate[index.astype(int), 1]
+            mX = np.mean(X)
+            mY = np.mean(Y)
+            length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+            angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+            polygon = cv2.ellipse2Poly(
+                (int(mY), int(mX)),
+                (int(length / 2), stickwidth),
+                int(angle),
+                0,
+                360,
+                1,
+            )
+            cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
+            canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
+    return canvas
+
+
+# image drawed by opencv is not good.
+def draw_handpose(canvas, all_hand_peaks, show_number=False):
+    edges = [
+        [0, 1],
+        [1, 2],
+        [2, 3],
+        [3, 4],
+        [0, 5],
+        [5, 6],
+        [6, 7],
+        [7, 8],
+        [0, 9],
+        [9, 10],
+        [10, 11],
+        [11, 12],
+        [0, 13],
+        [13, 14],
+        [14, 15],
+        [15, 16],
+        [0, 17],
+        [17, 18],
+        [18, 19],
+        [19, 20],
+    ]
+
+    for peaks in all_hand_peaks:
+        for ie, e in enumerate(edges):
+            if np.sum(np.all(peaks[e], axis=1) == 0) == 0:
+                x1, y1 = peaks[e[0]]
+                x2, y2 = peaks[e[1]]
+                cv2.line(
+                    canvas,
+                    (x1, y1),
+                    (x2, y2),
+                    matplotlib.colors.hsv_to_rgb(
+                        [ie / float(len(edges)), 1.0, 1.0]
+                    )
+                    * 255,
+                    thickness=2,
+                )
+
+        for i, keyponit in enumerate(peaks):
+            x, y = keyponit
+            cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+            if show_number:
+                cv2.putText(
+                    canvas,
+                    str(i),
+                    (x, y),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.3,
+                    (0, 0, 0),
+                    lineType=cv2.LINE_AA,
+                )
+    return canvas
+
+
+# detect hand according to body pose keypoints
+# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
+def handDetect(candidate, subset, oriImg):
+    # right hand: wrist 4, elbow 3, shoulder 2
+    # left hand: wrist 7, elbow 6, shoulder 5
+    ratioWristElbow = 0.33
+    detect_result = []
+    image_height, image_width = oriImg.shape[0:2]
+    for person in subset.astype(int):
+        # if any of three not detected
+        has_left = np.sum(person[[5, 6, 7]] == -1) == 0
+        has_right = np.sum(person[[2, 3, 4]] == -1) == 0
+        if not (has_left or has_right):
+            continue
+        hands = []
+        # left hand
+        if has_left:
+            left_shoulder_index, left_elbow_index, left_wrist_index = person[
+                [5, 6, 7]
+            ]
+            x1, y1 = candidate[left_shoulder_index][:2]
+            x2, y2 = candidate[left_elbow_index][:2]
+            x3, y3 = candidate[left_wrist_index][:2]
+            hands.append([x1, y1, x2, y2, x3, y3, True])
+        # right hand
+        if has_right:
+            (
+                right_shoulder_index,
+                right_elbow_index,
+                right_wrist_index,
+            ) = person[[2, 3, 4]]
+            x1, y1 = candidate[right_shoulder_index][:2]
+            x2, y2 = candidate[right_elbow_index][:2]
+            x3, y3 = candidate[right_wrist_index][:2]
+            hands.append([x1, y1, x2, y2, x3, y3, False])
+
+        for x1, y1, x2, y2, x3, y3, is_left in hands:
+            x = x3 + ratioWristElbow * (x3 - x2)
+            y = y3 + ratioWristElbow * (y3 - y2)
+            distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
+            distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+            width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
+            # x-y refers to the center --> offset to topLeft point
+            x -= width / 2
+            y -= width / 2  # width = height
+            # overflow the image
+            if x < 0:
+                x = 0
+            if y < 0:
+                y = 0
+            width1 = width
+            width2 = width
+            if x + width > image_width:
+                width1 = image_width - x
+            if y + width > image_height:
+                width2 = image_height - y
+            width = min(width1, width2)
+            # the max hand box value is 20 pixels
+            if width >= 20:
+                detect_result.append([int(x), int(y), int(width), is_left])
+
+    """
+    return value: [[x, y, w, True if left hand else False]].
+    width=height since the network require squared input.
+    x, y is the coordinate of top left 
+    """
+    return detect_result
+
+
+# get max index of 2d array
+def npmax(array):
+    arrayindex = array.argmax(1)
+    arrayvalue = array.max(1)
+    i = arrayvalue.argmax()
+    j = arrayindex[i]
+    return (i,)

apps/stable_diffusion/src/utils/stencils/stencil_utils.py

@@ -0,0 +1,186 @@
+import numpy as np
+from PIL import Image
+import torch
+from apps.stable_diffusion.src.utils.stencils import (
+    CannyDetector,
+    OpenposeDetector,
+)
+
+stencil = {}
+
+
+def HWC3(x):
+    assert x.dtype == np.uint8
+    if x.ndim == 2:
+        x = x[:, :, None]
+    assert x.ndim == 3
+    H, W, C = x.shape
+    assert C == 1 or C == 3 or C == 4
+    if C == 3:
+        return x
+    if C == 1:
+        return np.concatenate([x, x, x], axis=2)
+    if C == 4:
+        color = x[:, :, 0:3].astype(np.float32)
+        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
+        y = color * alpha + 255.0 * (1.0 - alpha)
+        y = y.clip(0, 255).astype(np.uint8)
+        return y
+
+
+def controlnet_hint_shaping(
+    controlnet_hint, height, width, dtype, num_images_per_prompt=1
+):
+    channels = 3
+    if isinstance(controlnet_hint, torch.Tensor):
+        # torch.Tensor: acceptble shape are any of chw, bchw(b==1) or bchw(b==num_images_per_prompt)
+        shape_chw = (channels, height, width)
+        shape_bchw = (1, channels, height, width)
+        shape_nchw = (num_images_per_prompt, channels, height, width)
+        if controlnet_hint.shape in [shape_chw, shape_bchw, shape_nchw]:
+            controlnet_hint = controlnet_hint.to(
+                dtype=dtype, device=torch.device("cpu")
+            )
+            if controlnet_hint.shape != shape_nchw:
+                controlnet_hint = controlnet_hint.repeat(
+                    num_images_per_prompt, 1, 1, 1
+                )
+            return controlnet_hint
+        else:
+            raise ValueError(
+                f"Acceptble shape of `stencil` are any of ({channels}, {height}, {width}),"
+                + f" (1, {channels}, {height}, {width}) or ({num_images_per_prompt}, "
+                + f"{channels}, {height}, {width}) but is {controlnet_hint.shape}"
+            )
+    elif isinstance(controlnet_hint, np.ndarray):
+        # np.ndarray: acceptable shape is any of hw, hwc, bhwc(b==1) or bhwc(b==num_images_per_promot)
+        # hwc is opencv compatible image format. Color channel must be BGR Format.
+        if controlnet_hint.shape == (height, width):
+            controlnet_hint = np.repeat(
+                controlnet_hint[:, :, np.newaxis], channels, axis=2
+            )  # hw -> hwc(c==3)
+        shape_hwc = (height, width, channels)
+        shape_bhwc = (1, height, width, channels)
+        shape_nhwc = (num_images_per_prompt, height, width, channels)
+        if controlnet_hint.shape in [shape_hwc, shape_bhwc, shape_nhwc]:
+            controlnet_hint = torch.from_numpy(controlnet_hint.copy())
+            controlnet_hint = controlnet_hint.to(
+                dtype=dtype, device=torch.device("cpu")
+            )
+            controlnet_hint /= 255.0
+            if controlnet_hint.shape != shape_nhwc:
+                controlnet_hint = controlnet_hint.repeat(
+                    num_images_per_prompt, 1, 1, 1
+                )
+            controlnet_hint = controlnet_hint.permute(
+                0, 3, 1, 2
+            )  # b h w c -> b c h w
+            return controlnet_hint
+        else:
+            raise ValueError(
+                f"Acceptble shape of `stencil` are any of ({width}, {channels}), "
+                + f"({height}, {width}, {channels}), "
+                + f"(1, {height}, {width}, {channels}) or "
+                + f"({num_images_per_prompt}, {channels}, {height}, {width}) but is {controlnet_hint.shape}"
+            )
+    elif isinstance(controlnet_hint, Image.Image):
+        if controlnet_hint.size == (width, height):
+            controlnet_hint = controlnet_hint.convert(
+                "RGB"
+            )  # make sure 3 channel RGB format
+            controlnet_hint = np.array(controlnet_hint)  # to numpy
+            controlnet_hint = controlnet_hint[:, :, ::-1]  # RGB -> BGR
+            return controlnet_hint_shaping(
+                controlnet_hint, height, width, num_images_per_prompt
+            )
+        else:
+            raise ValueError(
+                f"Acceptable image size of `stencil` is ({width}, {height}) but is {controlnet_hint.size}"
+            )
+    else:
+        raise ValueError(
+            f"Acceptable type of `stencil` are any of torch.Tensor, np.ndarray, PIL.Image.Image but is {type(controlnet_hint)}"
+        )
+
+
+def controlnet_hint_conversion(
+    image, use_stencil, height, width, dtype, num_images_per_prompt=1
+):
+    controlnet_hint = None
+    match use_stencil:
+        case "canny":
+            print("Detecting edge with canny")
+            controlnet_hint = hint_canny(image)
+        case "openpose":
+            print("Detecting human pose")
+            controlnet_hint = hint_openpose(image)
+        case "scribble":
+            print("Working with scribble")
+            controlnet_hint = hint_scribble(image)
+        case _:
+            return None
+    controlnet_hint = controlnet_hint_shaping(
+        controlnet_hint, height, width, dtype, num_images_per_prompt
+    )
+    return controlnet_hint
+
+
+stencil_to_model_id_map = {
+    "canny": "lllyasviel/sd-controlnet-canny",
+    "depth": "lllyasviel/sd-controlnet-depth",
+    "hed": "lllyasviel/sd-controlnet-hed",
+    "mlsd": "lllyasviel/sd-controlnet-mlsd",
+    "normal": "lllyasviel/sd-controlnet-normal",
+    "openpose": "lllyasviel/sd-controlnet-openpose",
+    "scribble": "lllyasviel/sd-controlnet-scribble",
+    "seg": "lllyasviel/sd-controlnet-seg",
+}
+
+
+def get_stencil_model_id(use_stencil):
+    if use_stencil in stencil_to_model_id_map:
+        return stencil_to_model_id_map[use_stencil]
+    return None
+
+
+# Stencil 1. Canny
+def hint_canny(
+    image: Image.Image,
+    low_threshold=100,
+    high_threshold=200,
+):
+    with torch.no_grad():
+        input_image = np.array(image)
+
+        if not "canny" in stencil:
+            stencil["canny"] = CannyDetector()
+        detected_map = stencil["canny"](
+            input_image, low_threshold, high_threshold
+        )
+        detected_map = HWC3(detected_map)
+        return detected_map
+
+
+# Stencil 2. OpenPose.
+def hint_openpose(
+    image: Image.Image,
+):
+    with torch.no_grad():
+        input_image = np.array(image)
+
+        if not "openpose" in stencil:
+            stencil["openpose"] = OpenposeDetector()
+
+        detected_map, _ = stencil["openpose"](input_image)
+        detected_map = HWC3(detected_map)
+        return detected_map
+
+
+# Stencil 3. Scribble.
+def hint_scribble(image: Image.Image):
+    with torch.no_grad():
+        input_image = np.array(image)
+
+        detected_map = np.zeros_like(input_image, dtype=np.uint8)
+        detected_map[np.min(input_image, axis=2) < 127] = 255
+        return detected_map

apps/stable_diffusion/src/utils/utils.py

@@ -0,0 +1,642 @@
+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
+import tempfile
+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.split("://", 1)[0]
+    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,
+    save_dir=tempfile.gettempdir(),
+    debug=False,
+    generate_vmfb=True,
+    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=model,
+        inputs=inputs,
+        is_f16=is_f16,
+        f16_input_mask=f16_input_mask,
+        debug=debug,
+        model_name=model_name,
+        save_dir=save_dir,
+    )
+    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",
+    )
+
+    if generate_vmfb:
+        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)
+
+    del mlir_module
+    gc.collect()
+
+
+def set_iree_runtime_flags():
+    vulkan_runtime_flags = [
+        f"--vulkan_large_heap_block_size={args.vulkan_large_heap_block_size}",
+        f"--vulkan_validation_layers={'true' if args.vulkan_validation_layers else 'false'}",
+    ]
+    if args.enable_rgp:
+        vulkan_runtime_flags += [
+            f"--enable_rgp=true",
+            f"--vulkan_debug_utils=true",
+        ]
+    set_iree_vulkan_runtime_flags(flags=vulkan_runtime_flags)
+
+
+def get_all_devices(driver_name):
+    """
+    Inputs: driver_name
+    Returns a list of all the available devices for a given driver sorted by
+    the iree path names of the device as in --list_devices option in iree.
+    """
+    from iree.runtime import get_driver
+
+    driver = get_driver(driver_name)
+    device_list_src = driver.query_available_devices()
+    device_list_src.sort(key=lambda d: d["path"])
+    return device_list_src
+
+
+def get_device_mapping(driver, key_combination=3):
+    """This method ensures consistent device ordering when choosing
+    specific devices for execution
+    Args:
+        driver (str): execution driver (vulkan, cuda, rocm, etc)
+        key_combination (int, optional): choice for mapping value for device name.
+        1 : path
+        2 : name
+        3 : (name, path)
+        Defaults to 3.
+    Returns:
+        dict: map to possible device names user can input mapped to desired combination of name/path.
+    """
+    from shark.iree_utils._common import iree_device_map
+
+    driver = iree_device_map(driver)
+    device_list = get_all_devices(driver)
+    device_map = dict()
+
+    def get_output_value(dev_dict):
+        if key_combination == 1:
+            return f"{driver}://{dev_dict['path']}"
+        if key_combination == 2:
+            return dev_dict["name"]
+        if key_combination == 3:
+            return (dev_dict["name"], f"{driver}://{dev_dict['path']}")
+
+    # mapping driver name to default device (driver://0)
+    device_map[f"{driver}"] = get_output_value(device_list[0])
+    for i, device in enumerate(device_list):
+        # mapping with index
+        device_map[f"{driver}://{i}"] = get_output_value(device)
+        # mapping with full path
+        device_map[f"{driver}://{device['path']}"] = get_output_value(device)
+    return device_map
+
+
+def map_device_to_name_path(device, key_combination=3):
+    """Gives the appropriate device data (supported name/path) for user selected execution device
+    Args:
+        device (str): user
+        key_combination (int, optional): choice for mapping value for device name.
+        1 : path
+        2 : name
+        3 : (name, path)
+        Defaults to 3.
+    Raises:
+        ValueError:
+    Returns:
+        str / tuple: returns the mapping str or tuple of mapping str for the device depending on key_combination value
+    """
+    driver = device.split("://")[0]
+    device_map = get_device_mapping(driver, key_combination)
+    try:
+        device_mapping = device_map[device]
+    except KeyError:
+        raise ValueError(f"Device '{device}' is not a valid device.")
+    return device_mapping
+
+
+def set_init_device_flags():
+    if "vulkan" in args.device:
+        # set runtime flags for vulkan.
+        set_iree_runtime_flags()
+
+        # set triple flag to avoid multiple calls to get_vulkan_triple_flag
+        device_name, args.device = map_device_to_name_path(args.device)
+        if not args.iree_vulkan_target_triple:
+            triple = get_vulkan_target_triple(device_name)
+            if triple is not None:
+                args.iree_vulkan_target_triple = triple
+        print(
+            f"Found device {device_name}. Using target triple {args.iree_vulkan_target_triple}."
+        )
+    elif "cuda" in args.device:
+        args.device = "cuda"
+    elif "cpu" in args.device:
+        args.device = "cpu"
+
+    # set max_length based on availability.
+    if args.hf_model_id in [
+        "Linaqruf/anything-v3.0",
+        "wavymulder/Analog-Diffusion",
+        "dreamlike-art/dreamlike-diffusion-1.0",
+    ]:
+        args.max_length = 77
+    elif args.hf_model_id == "prompthero/openjourney":
+        args.max_length = 64
+
+    # Use tuned models in the case of fp16, vulkan rdna3 or cuda sm devices.
+    if args.ckpt_loc != "":
+        base_model_id = fetch_and_update_base_model_id(args.ckpt_loc)
+    else:
+        base_model_id = fetch_and_update_base_model_id(args.hf_model_id)
+        if base_model_id == "":
+            base_model_id = args.hf_model_id
+
+    if (
+        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 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",
+        "runwayml/stable-diffusion-inpainting",
+        "stabilityai/stable-diffusion-2-inpainting",
+    ]:
+        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",
+    ]:
+        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, is_inpaint=False):
+    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"
+    )
+    num_in_channels = 9 if is_inpaint else 4
+    pipe = load_pipeline_from_original_stable_diffusion_ckpt(
+        checkpoint_path=custom_weights,
+        extract_ema=extract_ema,
+        from_safetensors=from_safetensors,
+        num_in_channels=num_in_channels,
+    )
+    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
+    model = "unet" if "stencil" 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, precision="fp32"):
+    vmfb_path = [
+        get_vmfb_path_name(extended_model_name[model])
+        for model in extended_model_name
+    ]
+    number_of_vmfbs = len(vmfb_path)
+    vmfb_present = [os.path.isfile(vmfb) for vmfb in vmfb_path]
+    all_vmfb_present = True
+    compiled_models = [None] * number_of_vmfbs
+
+    for i in range(number_of_vmfbs):
+        all_vmfb_present = all_vmfb_present and vmfb_present[i]
+
+    # We need to delete vmfbs only if some of the models were compiled.
+    if not all_vmfb_present:
+        for i in range(number_of_vmfbs):
+            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(number_of_vmfbs):
+            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, extra_info={}):
+    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 = Path(os.path.basename(args.ckpt_loc)).stem
+
+    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,
+    }
+
+    new_entry.update(extra_info)
+
+    with open(csv_path, "a", encoding="utf-8") 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)
+
+
+def get_generation_text_info(seeds, device):
+    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={args.batch_count}, batch_size={args.batch_size}, max_length={args.max_length}"
+
+    return text_output

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 +1,204 @@
+import os
+import sys
+
+if sys.platform == "darwin":
+    os.environ["DYLD_LIBRARY_PATH"] = "/usr/local/lib"
+
+import gradio as gr
+import apps.stable_diffusion.web.utils.global_obj as global_obj
+from apps.stable_diffusion.src import args, clear_all
+from apps.stable_diffusion.web.utils.gradio_configs import (
+    clear_gradio_tmp_imgs_folder,
+)
+from apps.stable_diffusion.web.ui.utils import get_custom_model_path
+
+# Clear all gradio tmp images from the last session
+clear_gradio_tmp_imgs_folder()
+# Create the custom model folder if it doesn't already exist
+get_custom_model_path().mkdir(parents=True, exist_ok=True)
+
+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,
+    txt2img_gallery,
+    txt2img_sendto_img2img,
+    txt2img_sendto_inpaint,
+    txt2img_sendto_outpaint,
+    txt2img_sendto_upscaler,
+    img2img_web,
+    img2img_gallery,
+    img2img_init_image,
+    img2img_sendto_inpaint,
+    img2img_sendto_outpaint,
+    img2img_sendto_upscaler,
+    inpaint_web,
+    inpaint_gallery,
+    inpaint_init_image,
+    inpaint_sendto_img2img,
+    inpaint_sendto_outpaint,
+    inpaint_sendto_upscaler,
+    outpaint_web,
+    outpaint_gallery,
+    outpaint_init_image,
+    outpaint_sendto_img2img,
+    outpaint_sendto_inpaint,
+    outpaint_sendto_upscaler,
+    upscaler_web,
+    upscaler_gallery,
+    upscaler_init_image,
+    upscaler_sendto_img2img,
+    upscaler_sendto_inpaint,
+    upscaler_sendto_outpaint,
+    lora_train_web,
+)
+
+# init global sd pipeline and config
+global_obj.init()
+
+
+def register_button_click(button, selectedid, inputs, outputs):
+    button.click(
+        lambda x: (
+            x[0]["name"] if len(x) != 0 else None,
+            gr.Tabs.update(selected=selectedid),
+        ),
+        inputs,
+        outputs,
+    )
+
+
+with gr.Blocks(
+    css=dark_theme, analytics_enabled=False, title="Stable Diffusion"
+) as sd_web:
+    with gr.Tabs() as tabs:
+        with gr.TabItem(label="Text-to-Image", id=0):
+            txt2img_web.render()
+        with gr.TabItem(label="Image-to-Image", id=1):
+            img2img_web.render()
+        with gr.TabItem(label="Inpainting", id=2):
+            inpaint_web.render()
+        with gr.TabItem(label="Outpainting", id=3):
+            outpaint_web.render()
+        with gr.TabItem(label="Upscaler", id=4):
+            upscaler_web.render()
+        with gr.TabItem(label="LoRA Training", id=5):
+            lora_train_web.render()
+
+    register_button_click(
+        txt2img_sendto_img2img,
+        1,
+        [txt2img_gallery],
+        [img2img_init_image, tabs],
+    )
+    register_button_click(
+        txt2img_sendto_inpaint,
+        2,
+        [txt2img_gallery],
+        [inpaint_init_image, tabs],
+    )
+    register_button_click(
+        txt2img_sendto_outpaint,
+        3,
+        [txt2img_gallery],
+        [outpaint_init_image, tabs],
+    )
+    register_button_click(
+        txt2img_sendto_upscaler,
+        4,
+        [txt2img_gallery],
+        [upscaler_init_image, tabs],
+    )
+    register_button_click(
+        img2img_sendto_inpaint,
+        2,
+        [img2img_gallery],
+        [inpaint_init_image, tabs],
+    )
+    register_button_click(
+        img2img_sendto_outpaint,
+        3,
+        [img2img_gallery],
+        [outpaint_init_image, tabs],
+    )
+    register_button_click(
+        img2img_sendto_upscaler,
+        4,
+        [img2img_gallery],
+        [upscaler_init_image, tabs],
+    )
+    register_button_click(
+        inpaint_sendto_img2img,
+        1,
+        [inpaint_gallery],
+        [img2img_init_image, tabs],
+    )
+    register_button_click(
+        inpaint_sendto_outpaint,
+        3,
+        [inpaint_gallery],
+        [outpaint_init_image, tabs],
+    )
+    register_button_click(
+        inpaint_sendto_upscaler,
+        4,
+        [inpaint_gallery],
+        [upscaler_init_image, tabs],
+    )
+    register_button_click(
+        outpaint_sendto_img2img,
+        1,
+        [outpaint_gallery],
+        [img2img_init_image, tabs],
+    )
+    register_button_click(
+        outpaint_sendto_inpaint,
+        2,
+        [outpaint_gallery],
+        [inpaint_init_image, tabs],
+    )
+    register_button_click(
+        outpaint_sendto_upscaler,
+        4,
+        [outpaint_gallery],
+        [upscaler_init_image, tabs],
+    )
+    register_button_click(
+        upscaler_sendto_img2img,
+        1,
+        [upscaler_gallery],
+        [img2img_init_image, tabs],
+    )
+    register_button_click(
+        upscaler_sendto_inpaint,
+        2,
+        [upscaler_gallery],
+        [inpaint_init_image, tabs],
+    )
+    register_button_click(
+        upscaler_sendto_outpaint,
+        3,
+        [upscaler_gallery],
+        [outpaint_init_image, tabs],
+    )
+
+
+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 +1,41 @@
+from apps.stable_diffusion.web.ui.txt2img_ui import (
+    txt2img_web,
+    txt2img_gallery,
+    txt2img_sendto_img2img,
+    txt2img_sendto_inpaint,
+    txt2img_sendto_outpaint,
+    txt2img_sendto_upscaler,
+)
+from apps.stable_diffusion.web.ui.img2img_ui import (
+    img2img_web,
+    img2img_gallery,
+    img2img_init_image,
+    img2img_sendto_inpaint,
+    img2img_sendto_outpaint,
+    img2img_sendto_upscaler,
+)
+from apps.stable_diffusion.web.ui.inpaint_ui import (
+    inpaint_web,
+    inpaint_gallery,
+    inpaint_init_image,
+    inpaint_sendto_img2img,
+    inpaint_sendto_outpaint,
+    inpaint_sendto_upscaler,
+)
+from apps.stable_diffusion.web.ui.outpaint_ui import (
+    outpaint_web,
+    outpaint_gallery,
+    outpaint_init_image,
+    outpaint_sendto_img2img,
+    outpaint_sendto_inpaint,
+    outpaint_sendto_upscaler,
+)
+from apps.stable_diffusion.web.ui.upscaler_ui import (
+    upscaler_web,
+    upscaler_gallery,
+    upscaler_init_image,
+    upscaler_sendto_img2img,
+    upscaler_sendto_inpaint,
+    upscaler_sendto_outpaint,
+)
+from apps.stable_diffusion.web.ui.lora_train_ui import lora_train_web

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

@@ -0,0 +1,198 @@
+/*
+Apply Gradio dark theme to the default Gradio theme.
+Procedure to upgrade the dark theme:
+- Using your browser, visit http://localhost:8080/?__theme=dark
+- Open your browser inspector, search for the .dark css class
+- Copy .dark class declarations, apply them here into :root
+*/
+
+:root {
+    --body-background-fill: var(--background-fill-primary);
+    --body-text-color: var(--neutral-100);
+    --color-accent-soft: var(--neutral-700);
+    --background-fill-primary: var(--neutral-950);
+    --background-fill-secondary: var(--neutral-900);
+    --border-color-accent: var(--neutral-600);
+    --border-color-primary: var(--neutral-700);
+    --link-text-color-active: var(--secondary-500);
+    --link-text-color: var(--secondary-500);
+    --link-text-color-hover: var(--secondary-400);
+    --link-text-color-visited: var(--secondary-600);
+    --body-text-color-subdued: var(--neutral-400);
+    --shadow-spread: 1px;
+    --block-background-fill: var(--neutral-800);
+    --block-border-color: var(--border-color-primary);
+    --block_border_width: None;
+    --block-info-text-color: var(--body-text-color-subdued);
+    --block-label-background-fill: var(--background-fill-secondary);
+    --block-label-border-color: var(--border-color-primary);
+    --block_label_border_width: None;
+    --block-label-text-color: var(--neutral-200);
+    --block_shadow: None;
+    --block_title_background_fill: None;
+    --block_title_border_color: None;
+    --block_title_border_width: None;
+    --block-title-text-color: var(--neutral-200);
+    --panel-background-fill: var(--background-fill-secondary);
+    --panel-border-color: var(--border-color-primary);
+    --panel_border_width: None;
+    --checkbox-background-color: var(--neutral-800);
+    --checkbox-background-color-focus: var(--checkbox-background-color);
+    --checkbox-background-color-hover: var(--checkbox-background-color);
+    --checkbox-background-color-selected: var(--secondary-600);
+    --checkbox-border-color: var(--neutral-700);
+    --checkbox-border-color-focus: var(--secondary-500);
+    --checkbox-border-color-hover: var(--neutral-600);
+    --checkbox-border-color-selected: var(--secondary-600);
+    --checkbox-border-width: var(--input-border-width);
+    --checkbox-label-background-fill: linear-gradient(to top, var(--neutral-900), var(--neutral-800));
+    --checkbox-label-background-fill-hover: linear-gradient(to top, var(--neutral-900), var(--neutral-800));
+    --checkbox-label-background-fill-selected: var(--checkbox-label-background-fill);
+    --checkbox-label-border-color: var(--border-color-primary);
+    --checkbox-label-border-color-hover: var(--checkbox-label-border-color);
+    --checkbox-label-border-width: var(--input-border-width);
+    --checkbox-label-text-color: var(--body-text-color);
+    --checkbox-label-text-color-selected: var(--checkbox-label-text-color);
+    --error-background-fill: var(--background-fill-primary);
+    --error-border-color: var(--border-color-primary);
+    --error_border_width: None;
+    --error-text-color: #ef4444;
+    --input-background-fill: var(--neutral-800);
+    --input-background-fill-focus: var(--secondary-600);
+    --input-background-fill-hover: var(--input-background-fill);
+    --input-border-color: var(--border-color-primary);
+    --input-border-color-focus: var(--neutral-700);
+    --input-border-color-hover: var(--input-border-color);
+    --input_border_width: None;
+    --input-placeholder-color: var(--neutral-500);
+    --input_shadow: None;
+    --input-shadow-focus: 0 0 0 var(--shadow-spread) var(--neutral-700), var(--shadow-inset);
+    --loader_color: None;
+    --slider_color: None;
+    --stat-background-fill: linear-gradient(to right, var(--primary-400), var(--primary-600));
+    --table-border-color: var(--neutral-700);
+    --table-even-background-fill: var(--neutral-950);
+    --table-odd-background-fill: var(--neutral-900);
+    --table-row-focus: var(--color-accent-soft);
+    --button-border-width: var(--input-border-width);
+    --button-cancel-background-fill: linear-gradient(to bottom right, #dc2626, #b91c1c);
+    --button-cancel-background-fill-hover: linear-gradient(to bottom right, #dc2626, #dc2626);
+    --button-cancel-border-color: #dc2626;
+    --button-cancel-border-color-hover: var(--button-cancel-border-color);
+    --button-cancel-text-color: white;
+    --button-cancel-text-color-hover: var(--button-cancel-text-color);
+    --button-primary-background-fill: linear-gradient(to bottom right, var(--primary-500), var(--primary-600));
+    --button-primary-background-fill-hover: linear-gradient(to bottom right, var(--primary-500), var(--primary-500));
+    --button-primary-border-color: var(--primary-500);
+    --button-primary-border-color-hover: var(--button-primary-border-color);
+    --button-primary-text-color: white;
+    --button-primary-text-color-hover: var(--button-primary-text-color);
+    --button-secondary-background-fill: linear-gradient(to bottom right, var(--neutral-600), var(--neutral-700));
+    --button-secondary-background-fill-hover: linear-gradient(to bottom right, var(--neutral-600), var(--neutral-600));
+    --button-secondary-border-color: var(--neutral-600);
+    --button-secondary-border-color-hover: var(--button-secondary-border-color);
+    --button-secondary-text-color: white;
+    --button-secondary-text-color-hover: var(--button-secondary-text-color);
+    --block-border-width: 1px;
+    --block-label-border-width: 1px;
+    --form-gap-width: 1px;
+    --error-border-width: 1px;
+    --input-border-width: 1px;
+}
+
+/* SHARK theme */
+
+/* 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(--background-fill-primary) !important;
+}
+
+#prompt_examples {
+    margin: 0 !important;
+}
+
+#prompt_examples svg {
+    display: none !important;
+}
+
+#ui_body {
+    padding: var(--size-2) !important;
+    border-radius: 0.5em !important;
+}
+
+#img_result+div {
+    display: none !important;
+}
+
+footer {
+    display: none !important;
+}
+
+#gallery + div {
+    border-radius: 0 !important;
+}
+
+/* Prevent progress bar to block gallery navigation while building images (Gradio V3.19.0) */
+#gallery .wrap.default {
+    pointer-events: none;
+}
+
+/* Import Png info box */
+#txt2img_prompt_image .fixed-height {
+    height: var(--size-32);
+}
+
+/* Hide "remove buttons" from ui dropdowns */
+#custom_model .token-remove.remove-all,
+#scheduler .token-remove.remove-all,
+#device .token-remove.remove-all,
+#stencil_model .token-remove.remove-all {
+    display: none;
+}
+
+/* Hide selected items from ui dropdowns */
+#custom_model .options .item .inner-item,
+#scheduler .options .item .inner-item,
+#device .options .item .inner-item,
+#stencil_model .options .item .inner-item {
+    display:none;
+}
+
+/* Hide the download icon from the nod logo */
+#top_logo .download {
+    display: none;
+}

apps/stable_diffusion/web/ui/img2img_ui.py

@@ -0,0 +1,261 @@
+from pathlib import Path
+import os
+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,
+    get_custom_model_path,
+    get_custom_model_files,
+    scheduler_list,
+    predefined_models,
+    cancel_sd,
+)
+
+
+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():
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {get_custom_model_path()})",
+                        elem_id="custom_model",
+                        value=os.path.basename(args.ckpt_loc)
+                        if args.ckpt_loc
+                        else "None",
+                        choices=["None"]
+                        + get_custom_model_files()
+                        + predefined_models,
+                    )
+                    hf_model_id = gr.Textbox(
+                        elem_id="hf_model_id",
+                        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",
+                    )
+
+                img2img_init_image = gr.Image(
+                    label="Input Image", type="pil"
+                ).style(height=300)
+
+                with gr.Accordion(label="Stencil Options", open=False):
+                    with gr.Row():
+                        use_stencil = gr.Dropdown(
+                            elem_id="stencil_model",
+                            label="Stencil model",
+                            value="None",
+                            choices=["None", "canny", "openpose", "scribble"],
+                        )
+                with gr.Accordion(label="LoRA Options", open=False):
+                    with gr.Row():
+                        lora_weights = gr.Dropdown(
+                            label=f"Standlone LoRA weights (Path: {get_custom_model_path()})",
+                            elem_id="lora_weights",
+                            value="None",
+                            choices=["None"] + get_custom_model_files(),
+                        )
+                        lora_hf_id = gr.Textbox(
+                            elem_id="lora_hf_id",
+                            placeholder="Select 'None' in the Standlone LoRA weights dropdown on the left if you want to use a standalone HuggingFace model ID for LoRA here e.g: sayakpaul/sd-model-finetuned-lora-t4",
+                            value="",
+                            label="HuggingFace Model ID",
+                            lines=3,
+                        )
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            elem_id="scheduler",
+                            label="Scheduler",
+                            value="PNDM",
+                            choices=scheduler_list,
+                        )
+                        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, 768, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 768, value=args.width, step=8, label="Width"
+                        )
+                        precision = gr.Radio(
+                            label="Precision",
+                            value=args.precision,
+                            choices=[
+                                "fp16",
+                                "fp32",
+                            ],
+                            visible=True,
+                        )
+                        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=0.01,
+                            label="Denoising Strength",
+                        )
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            guidance_scale = gr.Slider(
+                                0,
+                                50,
+                                value=args.guidance_scale,
+                                step=0.1,
+                                label="CFG Scale",
+                            )
+                        with gr.Column(scale=3):
+                            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,
+                        )
+                        stop_batch = gr.Button("Stop Batch")
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        elem_id="device",
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        random_seed = gr.Button("Randomize Seed")
+                        random_seed.click(
+                            None,
+                            inputs=[],
+                            outputs=[seed],
+                            _js="() => -1",
+                        )
+                    with gr.Column(scale=6):
+                        stable_diffusion = gr.Button("Generate Image(s)")
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    img2img_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,
+                )
+                with gr.Row():
+                    img2img_sendto_inpaint = gr.Button(value="SendTo Inpaint")
+                    img2img_sendto_outpaint = gr.Button(
+                        value="SendTo Outpaint"
+                    )
+                    img2img_sendto_upscaler = gr.Button(
+                        value="SendTo Upscaler"
+                    )
+
+        kwargs = dict(
+            fn=img2img_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                img2img_init_image,
+                height,
+                width,
+                steps,
+                strength,
+                guidance_scale,
+                seed,
+                batch_count,
+                batch_size,
+                scheduler,
+                custom_model,
+                hf_model_id,
+                precision,
+                device,
+                max_length,
+                use_stencil,
+                save_metadata_to_json,
+                save_metadata_to_png,
+                lora_weights,
+                lora_hf_id,
+            ],
+            outputs=[img2img_gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt_submit = prompt.submit(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**kwargs)
+        generate_click = stable_diffusion.click(**kwargs)
+        stop_batch.click(
+            fn=cancel_sd,
+            cancels=[prompt_submit, neg_prompt_submit, generate_click],
+        )

apps/stable_diffusion/web/ui/inpaint_ui.py

@@ -0,0 +1,263 @@
+from pathlib import Path
+import os
+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,
+    get_custom_model_path,
+    get_custom_model_files,
+    scheduler_list,
+    predefined_paint_models,
+    cancel_sd,
+)
+
+
+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():
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {get_custom_model_path()})",
+                        elem_id="custom_model",
+                        value=os.path.basename(args.ckpt_loc)
+                        if args.ckpt_loc
+                        else "None",
+                        choices=["None"]
+                        + get_custom_model_files()
+                        + predefined_paint_models,
+                    )
+                    hf_model_id = gr.Textbox(
+                        elem_id="hf_model_id",
+                        placeholder="Select 'None' in the Models dropdown on the left and enter model ID here e.g: ghunkins/stable-diffusion-liberty-inpainting",
+                        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",
+                    )
+
+                inpaint_init_image = gr.Image(
+                    label="Masked Image",
+                    source="upload",
+                    tool="sketch",
+                    type="pil",
+                ).style(height=350)
+
+                with gr.Accordion(label="LoRA Options", open=False):
+                    with gr.Row():
+                        lora_weights = gr.Dropdown(
+                            label=f"Standlone LoRA weights (Path: {get_custom_model_path()})",
+                            elem_id="lora_weights",
+                            value="None",
+                            choices=["None"] + get_custom_model_files(),
+                        )
+                        lora_hf_id = gr.Textbox(
+                            elem_id="lora_hf_id",
+                            placeholder="Select 'None' in the Standlone LoRA weights dropdown on the left if you want to use a standalone HuggingFace model ID for LoRA here e.g: sayakpaul/sd-model-finetuned-lora-t4",
+                            value="",
+                            label="HuggingFace Model ID",
+                            lines=3,
+                        )
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            elem_id="scheduler",
+                            label="Scheduler",
+                            value="PNDM",
+                            choices=scheduler_list,
+                        )
+                        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, 768, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 768, 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():
+                        inpaint_full_res = gr.Radio(
+                            choices=["Whole picture", "Only masked"],
+                            type="index",
+                            value="Whole picture",
+                            label="Inpaint area",
+                        )
+                        inpaint_full_res_padding = gr.Slider(
+                            minimum=0,
+                            maximum=256,
+                            step=4,
+                            value=32,
+                            label="Only masked padding, pixels",
+                        )
+                    with gr.Row():
+                        steps = gr.Slider(
+                            1, 100, value=args.steps, step=1, label="Steps"
+                        )
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            guidance_scale = gr.Slider(
+                                0,
+                                50,
+                                value=args.guidance_scale,
+                                step=0.1,
+                                label="CFG Scale",
+                            )
+                        with gr.Column(scale=3):
+                            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,
+                        )
+                        stop_batch = gr.Button("Stop Batch")
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        elem_id="device",
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        random_seed = gr.Button("Randomize Seed")
+                        random_seed.click(
+                            None,
+                            inputs=[],
+                            outputs=[seed],
+                            _js="() => -1",
+                        )
+                    with gr.Column(scale=6):
+                        stable_diffusion = gr.Button("Generate Image(s)")
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    inpaint_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,
+                )
+                with gr.Row():
+                    inpaint_sendto_img2img = gr.Button(value="SendTo Img2Img")
+                    inpaint_sendto_outpaint = gr.Button(
+                        value="SendTo Outpaint"
+                    )
+                    inpaint_sendto_upscaler = gr.Button(
+                        value="SendTo Upscaler"
+                    )
+
+        kwargs = dict(
+            fn=inpaint_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                inpaint_init_image,
+                height,
+                width,
+                inpaint_full_res,
+                inpaint_full_res_padding,
+                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,
+                lora_weights,
+                lora_hf_id,
+            ],
+            outputs=[inpaint_gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt_submit = prompt.submit(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**kwargs)
+        generate_click = stable_diffusion.click(**kwargs)
+        stop_batch.click(
+            fn=cancel_sd,
+            cancels=[prompt_submit, neg_prompt_submit, generate_click],
+        )

apps/stable_diffusion/web/ui/lora_train_ui.py

@@ -0,0 +1,205 @@
+from pathlib import Path
+import os
+import gradio as gr
+from PIL import Image
+from apps.stable_diffusion.scripts import lora_train
+from apps.stable_diffusion.src import prompt_examples, args
+from apps.stable_diffusion.web.ui.utils import (
+    available_devices,
+    nodlogo_loc,
+    get_custom_model_path,
+    get_custom_model_files,
+    scheduler_list_txt2img,
+    predefined_models,
+)
+
+with gr.Blocks(title="Lora Training") as lora_train_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():
+                    with gr.Column(scale=10):
+                        with gr.Row():
+                            custom_model = gr.Dropdown(
+                                label=f"Models (Custom Model path: {get_custom_model_path()})",
+                                elem_id="custom_model",
+                                value=os.path.basename(args.ckpt_loc)
+                                if args.ckpt_loc
+                                else "None",
+                                choices=["None"]
+                                + get_custom_model_files()
+                                + predefined_models,
+                            )
+                            hf_model_id = gr.Textbox(
+                                elem_id="hf_model_id",
+                                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="image_dir_box_outer"):
+                    training_images_dir = gr.Textbox(
+                        label="ImageDirectory",
+                        value=args.training_images_dir,
+                        lines=1,
+                        elem_id="prompt_box",
+                    )
+                with gr.Group(elem_id="prompt_box_outer"):
+                    prompt = gr.Textbox(
+                        label="Prompt",
+                        value=args.prompts[0],
+                        lines=1,
+                        elem_id="prompt_box",
+                    )
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            elem_id="scheduler",
+                            label="Scheduler",
+                            value=args.scheduler,
+                            choices=scheduler_list_txt2img,
+                        )
+                    with gr.Row():
+                        height = gr.Slider(
+                            384, 768, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 768, 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,
+                            2000,
+                            value=args.training_steps,
+                            step=1,
+                            label="Training Steps",
+                        )
+                        guidance_scale = gr.Slider(
+                            0,
+                            50,
+                            value=args.guidance_scale,
+                            step=0.1,
+                            label="CFG Scale",
+                        )
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            batch_count = gr.Slider(
+                                1,
+                                100,
+                                value=args.batch_count,
+                                step=1,
+                                label="Batch Count",
+                                interactive=True,
+                            )
+                        with gr.Column(scale=3):
+                            batch_size = gr.Slider(
+                                1,
+                                4,
+                                value=args.batch_size,
+                                step=1,
+                                label="Batch Size",
+                                interactive=True,
+                            )
+                        stop_batch = gr.Button("Stop Batch")
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        elem_id="device",
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        random_seed = gr.Button("Randomize Seed")
+                        random_seed.click(
+                            None,
+                            inputs=[],
+                            outputs=[seed],
+                            _js="() => -1",
+                        )
+                    with gr.Column(scale=6):
+                        train_lora = gr.Button("Train LoRA")
+
+                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():
+                    std_output = gr.Textbox(
+                        value="Nothing to show.",
+                        lines=1,
+                        show_label=False,
+                    )
+                lora_save_dir = (
+                    args.lora_save_dir if args.lora_save_dir else Path.cwd()
+                )
+                lora_save_dir = Path(lora_save_dir, "lora")
+                output_loc = gr.Textbox(
+                    label="Saving Lora at",
+                    value=lora_save_dir,
+                )
+
+        kwargs = dict(
+            fn=lora_train,
+            inputs=[
+                prompt,
+                height,
+                width,
+                steps,
+                guidance_scale,
+                seed,
+                batch_count,
+                batch_size,
+                scheduler,
+                custom_model,
+                hf_model_id,
+                precision,
+                device,
+                max_length,
+                training_images_dir,
+                output_loc,
+            ],
+            outputs=[std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt_submit = prompt.submit(**kwargs)
+        train_click = train_lora.click(**kwargs)
+        stop_batch.click(fn=None, cancels=[prompt_submit, train_click])

apps/stable_diffusion/web/ui/outpaint_ui.py

@@ -0,0 +1,283 @@
+from pathlib import Path
+import os
+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,
+    get_custom_model_path,
+    get_custom_model_files,
+    scheduler_list,
+    predefined_paint_models,
+    cancel_sd,
+)
+
+
+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():
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {get_custom_model_path()})",
+                        elem_id="custom_model",
+                        value=os.path.basename(args.ckpt_loc)
+                        if args.ckpt_loc
+                        else "None",
+                        choices=["None"]
+                        + get_custom_model_files()
+                        + predefined_paint_models,
+                    )
+                    hf_model_id = gr.Textbox(
+                        elem_id="hf_model_id",
+                        placeholder="Select 'None' in the Models dropdown on the left and enter model ID here e.g: ghunkins/stable-diffusion-liberty-inpainting",
+                        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",
+                    )
+
+                outpaint_init_image = gr.Image(
+                    label="Input Image", type="pil"
+                ).style(height=300)
+
+                with gr.Accordion(label="LoRA Options", open=False):
+                    with gr.Row():
+                        lora_weights = gr.Dropdown(
+                            label=f"Standlone LoRA weights (Path: {get_custom_model_path()})",
+                            elem_id="lora_weights",
+                            value="None",
+                            choices=["None"] + get_custom_model_files(),
+                        )
+                        lora_hf_id = gr.Textbox(
+                            elem_id="lora_hf_id",
+                            placeholder="Select 'None' in the Standlone LoRA weights dropdown on the left if you want to use a standalone HuggingFace model ID for LoRA here e.g: sayakpaul/sd-model-finetuned-lora-t4",
+                            value="",
+                            label="HuggingFace Model ID",
+                            lines=3,
+                        )
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            elem_id="scheduler",
+                            label="Scheduler",
+                            value="PNDM",
+                            choices=scheduler_list,
+                        )
+                        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, 768, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 768, 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():
+                        with gr.Column(scale=3):
+                            guidance_scale = gr.Slider(
+                                0,
+                                50,
+                                value=args.guidance_scale,
+                                step=0.1,
+                                label="CFG Scale",
+                            )
+                        with gr.Column(scale=3):
+                            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,
+                        )
+                        stop_batch = gr.Button("Stop Batch")
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        elem_id="device",
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        random_seed = gr.Button("Randomize Seed")
+                        random_seed.click(
+                            None,
+                            inputs=[],
+                            outputs=[seed],
+                            _js="() => -1",
+                        )
+                    with gr.Column(scale=6):
+                        stable_diffusion = gr.Button("Generate Image(s)")
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    outpaint_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,
+                )
+                with gr.Row():
+                    outpaint_sendto_img2img = gr.Button(value="SendTo Img2Img")
+                    outpaint_sendto_inpaint = gr.Button(value="SendTo Inpaint")
+                    outpaint_sendto_upscaler = gr.Button(
+                        value="SendTo Upscaler"
+                    )
+
+        kwargs = dict(
+            fn=outpaint_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                outpaint_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,
+                lora_weights,
+                lora_hf_id,
+            ],
+            outputs=[outpaint_gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt_submit = prompt.submit(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**kwargs)
+        generate_click = stable_diffusion.click(**kwargs)
+        stop_batch.click(
+            fn=cancel_sd,
+            cancels=[prompt_submit, neg_prompt_submit, generate_click],
+        )

apps/stable_diffusion/web/ui/txt2img_ui.py

@@ -0,0 +1,279 @@
+from pathlib import Path
+import os
+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,
+    get_custom_model_path,
+    get_custom_model_files,
+    scheduler_list_txt2img,
+    predefined_models,
+    cancel_sd,
+)
+
+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():
+                    with gr.Column(scale=10):
+                        with gr.Row():
+                            custom_model = gr.Dropdown(
+                                label=f"Models (Custom Model path: {get_custom_model_path()})",
+                                elem_id="custom_model",
+                                value=os.path.basename(args.ckpt_loc)
+                                if args.ckpt_loc
+                                else "None",
+                                choices=["None"]
+                                + get_custom_model_files()
+                                + predefined_models,
+                            )
+                            hf_model_id = gr.Textbox(
+                                elem_id="hf_model_id",
+                                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.Column(scale=1, min_width=170):
+                        png_info_img = gr.Image(
+                            label="Import PNG info",
+                            elem_id="txt2img_prompt_image",
+                            type="pil",
+                            tool="None",
+                            visible=True,
+                        )
+
+                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="LoRA Options", open=False):
+                    with gr.Row():
+                        lora_weights = gr.Dropdown(
+                            label=f"Standlone LoRA weights (Path: {get_custom_model_path()})",
+                            elem_id="lora_weights",
+                            value="None",
+                            choices=["None"] + get_custom_model_files(),
+                        )
+                        lora_hf_id = gr.Textbox(
+                            elem_id="lora_hf_id",
+                            placeholder="Select 'None' in the Standlone LoRA weights dropdown on the left if you want to use a standalone HuggingFace model ID for LoRA here e.g: sayakpaul/sd-model-finetuned-lora-t4",
+                            value="",
+                            label="HuggingFace Model ID",
+                            lines=3,
+                        )
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            elem_id="scheduler",
+                            label="Scheduler",
+                            value=args.scheduler,
+                            choices=scheduler_list_txt2img,
+                        )
+                        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, 768, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 768, 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():
+                        with gr.Column(scale=3):
+                            batch_count = gr.Slider(
+                                1,
+                                100,
+                                value=args.batch_count,
+                                step=1,
+                                label="Batch Count",
+                                interactive=True,
+                            )
+                        with gr.Column(scale=3):
+                            batch_size = gr.Slider(
+                                1,
+                                4,
+                                value=args.batch_size,
+                                step=1,
+                                label="Batch Size",
+                                interactive=True,
+                            )
+                        stop_batch = gr.Button("Stop Batch")
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        elem_id="device",
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        random_seed = gr.Button("Randomize Seed")
+                        random_seed.click(
+                            None,
+                            inputs=[],
+                            outputs=[seed],
+                            _js="() => -1",
+                        )
+                    with gr.Column(scale=6):
+                        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():
+                    txt2img_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,
+                )
+                with gr.Row():
+                    txt2img_sendto_img2img = gr.Button(value="SendTo Img2Img")
+                    txt2img_sendto_inpaint = gr.Button(value="SendTo Inpaint")
+                    txt2img_sendto_outpaint = gr.Button(
+                        value="SendTo Outpaint"
+                    )
+                    txt2img_sendto_upscaler = gr.Button(
+                        value="SendTo Upscaler"
+                    )
+
+        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,
+                lora_weights,
+                lora_hf_id,
+            ],
+            outputs=[txt2img_gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt_submit = prompt.submit(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**kwargs)
+        generate_click = stable_diffusion.click(**kwargs)
+        stop_batch.click(
+            fn=cancel_sd,
+            cancels=[prompt_submit, neg_prompt_submit, generate_click],
+        )
+
+        from apps.stable_diffusion.web.utils.png_metadata import (
+            import_png_metadata,
+        )
+
+        png_info_img.change(
+            fn=import_png_metadata,
+            inputs=[
+                png_info_img,
+            ],
+            outputs=[
+                png_info_img,
+                prompt,
+                negative_prompt,
+                steps,
+                scheduler,
+                guidance_scale,
+                seed,
+                width,
+                height,
+                custom_model,
+                hf_model_id,
+            ],
+        )

apps/stable_diffusion/web/ui/upscaler_ui.py

@@ -0,0 +1,239 @@
+from pathlib import Path
+import os
+import gradio as gr
+from PIL import Image
+from apps.stable_diffusion.scripts import upscaler_inf
+from apps.stable_diffusion.src import args
+from apps.stable_diffusion.web.ui.utils import (
+    available_devices,
+    nodlogo_loc,
+    get_custom_model_path,
+    get_custom_model_files,
+    scheduler_list,
+    predefined_upscaler_models,
+)
+
+
+with gr.Blocks(title="Upscaler") as upscaler_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():
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {get_custom_model_path()})",
+                        elem_id="custom_model",
+                        value=os.path.basename(args.ckpt_loc)
+                        if args.ckpt_loc
+                        else "None",
+                        choices=["None"]
+                        + get_custom_model_files()
+                        + predefined_upscaler_models,
+                    )
+                    hf_model_id = gr.Textbox(
+                        elem_id="hf_model_id",
+                        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",
+                    )
+
+                upscaler_init_image = gr.Image(
+                    label="Input Image", type="pil"
+                ).style(height=300)
+
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            elem_id="scheduler",
+                            label="Scheduler",
+                            value="DDIM",
+                            choices=scheduler_list,
+                        )
+                        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(
+                            128,
+                            512,
+                            value=args.height,
+                            step=128,
+                            label="Height",
+                        )
+                        width = gr.Slider(
+                            128,
+                            512,
+                            value=args.width,
+                            step=128,
+                            label="Width",
+                        )
+                        precision = gr.Radio(
+                            label="Precision",
+                            value=args.precision,
+                            choices=[
+                                "fp16",
+                                "fp32",
+                            ],
+                            visible=True,
+                        )
+                        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"
+                        )
+                        noise_level = gr.Slider(
+                            0,
+                            100,
+                            value=args.noise_level,
+                            step=1,
+                            label="Noise Level",
+                        )
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            guidance_scale = gr.Slider(
+                                0,
+                                50,
+                                value=args.guidance_scale,
+                                step=0.1,
+                                label="CFG Scale",
+                            )
+                        with gr.Column(scale=3):
+                            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,
+                        )
+                        stop_batch = gr.Button("Stop Batch")
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        elem_id="device",
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        random_seed = gr.Button("Randomize Seed")
+                        random_seed.click(
+                            None,
+                            inputs=[],
+                            outputs=[seed],
+                            _js="() => -1",
+                        )
+                    with gr.Column(scale=6):
+                        stable_diffusion = gr.Button("Generate Image(s)")
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    upscaler_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,
+                )
+                with gr.Row():
+                    upscaler_sendto_img2img = gr.Button(value="SendTo Img2Img")
+                    upscaler_sendto_inpaint = gr.Button(value="SendTo Inpaint")
+                    upscaler_sendto_outpaint = gr.Button(
+                        value="SendTo Outpaint"
+                    )
+
+        kwargs = dict(
+            fn=upscaler_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                upscaler_init_image,
+                height,
+                width,
+                steps,
+                noise_level,
+                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=[upscaler_gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt_submit = prompt.submit(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**kwargs)
+        generate_click = stable_diffusion.click(**kwargs)
+        stop_batch.click(
+            fn=None, cancels=[prompt_submit, neg_prompt_submit, generate_click]
+        )

apps/stable_diffusion/web/ui/utils.py

@@ -0,0 +1,105 @@
+import os
+import sys
+from apps.stable_diffusion.src import get_available_devices
+import glob
+from pathlib import Path
+from apps.stable_diffusion.src import args
+from dataclasses import dataclass
+import apps.stable_diffusion.web.utils.global_obj as global_obj
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+    SD_STATE_CANCEL,
+)
+
+
+@dataclass
+class Config:
+    mode: str
+    model_id: str
+    ckpt_loc: str
+    precision: str
+    batch_size: int
+    max_length: int
+    height: int
+    width: int
+    device: str
+    use_lora: str
+    use_stencil: str
+
+
+custom_model_filetypes = (
+    "*.ckpt",
+    "*.safetensors",
+)  # the tuple of file types
+
+scheduler_list = [
+    "DDIM",
+    "PNDM",
+    "DPMSolverMultistep",
+    "EulerAncestralDiscrete",
+]
+scheduler_list_txt2img = [
+    "DDIM",
+    "PNDM",
+    "LMSDiscrete",
+    "KDPM2Discrete",
+    "DPMSolverMultistep",
+    "EulerDiscrete",
+    "EulerAncestralDiscrete",
+    "SharkEulerDiscrete",
+]
+
+predefined_models = [
+    "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",
+]
+
+predefined_paint_models = [
+    "runwayml/stable-diffusion-inpainting",
+    "stabilityai/stable-diffusion-2-inpainting",
+]
+predefined_upscaler_models = [
+    "stabilityai/stable-diffusion-x4-upscaler",
+]
+
+
+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_custom_model_path():
+    return Path(args.ckpt_dir) if args.ckpt_dir else Path(Path.cwd(), "models")
+
+
+def get_custom_model_pathfile(custom_model_name):
+    return os.path.join(get_custom_model_path(), custom_model_name)
+
+
+def get_custom_model_files():
+    ckpt_files = []
+    for extn in custom_model_filetypes:
+        files = [
+            os.path.basename(x)
+            for x in glob.glob(os.path.join(get_custom_model_path(), extn))
+        ]
+        ckpt_files.extend(files)
+    return sorted(ckpt_files, key=str.casefold)
+
+
+def cancel_sd():
+    # Try catch it, as gc can delete global_obj.sd_obj while switching model
+    try:
+        global_obj.set_sd_status(SD_STATE_CANCEL)
+    except Exception:
+        pass
+
+
+nodlogo_loc = resource_path("logos/nod-logo.png")
+available_devices = get_available_devices()

apps/stable_diffusion/web/utils/global_obj.py

@@ -0,0 +1,56 @@
+import gc
+
+
+"""
+The global objects include SD pipeline and config.
+Maintaining the global objects would avoid creating extra pipeline objects when switching modes.
+Also we could avoid memory leak when switching models by clearing the cache.
+"""
+
+
+def init():
+    global sd_obj
+    global config_obj
+    sd_obj = None
+    config_obj = None
+
+
+def set_sd_obj(value):
+    global sd_obj
+    sd_obj = value
+
+
+def set_cfg_obj(value):
+    global config_obj
+    config_obj = value
+
+
+def set_schedulers(value):
+    global sd_obj
+    sd_obj.scheduler = value
+
+
+def get_sd_obj():
+    return sd_obj
+
+
+def get_cfg_obj():
+    return config_obj
+
+
+def set_sd_status(value):
+    global sd_obj
+    sd_obj.status = value
+
+
+def get_sd_status():
+    global sd_obj
+    return sd_obj.status
+
+
+def clear_cache():
+    global sd_obj
+    global config_obj
+    del sd_obj
+    del config_obj
+    gc.collect()

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

apps/stable_diffusion/web/utils/png_metadata.py

@@ -0,0 +1,148 @@
+import re
+from pathlib import Path
+from apps.stable_diffusion.web.ui.txt2img_ui import (
+    png_info_img,
+    prompt,
+    negative_prompt,
+    steps,
+    scheduler,
+    guidance_scale,
+    seed,
+    width,
+    height,
+    custom_model,
+    hf_model_id,
+)
+from apps.stable_diffusion.web.ui.utils import (
+    get_custom_model_pathfile,
+    scheduler_list_txt2img,
+    predefined_models,
+)
+
+re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
+re_param = re.compile(re_param_code)
+re_imagesize = re.compile(r"^(\d+)x(\d+)$")
+
+
+def parse_generation_parameters(x: str):
+    res = {}
+    prompt = ""
+    negative_prompt = ""
+    done_with_prompt = False
+
+    *lines, lastline = x.strip().split("\n")
+    if len(re_param.findall(lastline)) < 3:
+        lines.append(lastline)
+        lastline = ""
+
+    for i, line in enumerate(lines):
+        line = line.strip()
+        if line.startswith("Negative prompt:"):
+            done_with_prompt = True
+            line = line[16:].strip()
+
+        if done_with_prompt:
+            negative_prompt += ("" if negative_prompt == "" else "\n") + line
+        else:
+            prompt += ("" if prompt == "" else "\n") + line
+
+    res["Prompt"] = prompt
+    res["Negative prompt"] = negative_prompt
+
+    for k, v in re_param.findall(lastline):
+        v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
+        m = re_imagesize.match(v)
+        if m is not None:
+            res[k + "-1"] = m.group(1)
+            res[k + "-2"] = m.group(2)
+        else:
+            res[k] = v
+
+    # Missing CLIP skip means it was set to 1 (the default)
+    if "Clip skip" not in res:
+        res["Clip skip"] = "1"
+
+    hypernet = res.get("Hypernet", None)
+    if hypernet is not None:
+        res[
+            "Prompt"
+        ] += f"""<hypernet:{hypernet}:{res.get("Hypernet strength", "1.0")}>"""
+
+    if "Hires resize-1" not in res:
+        res["Hires resize-1"] = 0
+        res["Hires resize-2"] = 0
+
+    return res
+
+
+def import_png_metadata(pil_data):
+    try:
+        png_info = pil_data.info["parameters"]
+        metadata = parse_generation_parameters(png_info)
+        png_hf_model_id = ""
+        png_custom_model = ""
+
+        if "Model" in metadata:
+            # Remove extension from model info
+            if metadata["Model"].endswith(".safetensors") or metadata[
+                "Model"
+            ].endswith(".ckpt"):
+                metadata["Model"] = Path(metadata["Model"]).stem
+            # Check for the model name match with one of the local ckpt or safetensors files
+            if Path(
+                get_custom_model_pathfile(metadata["Model"] + ".ckpt")
+            ).is_file():
+                png_custom_model = metadata["Model"] + ".ckpt"
+            if Path(
+                get_custom_model_pathfile(metadata["Model"] + ".safetensors")
+            ).is_file():
+                png_custom_model = metadata["Model"] + ".safetensors"
+            # Check for a model match with one of the default model list (ex: "Linaqruf/anything-v3.0")
+            if metadata["Model"] in predefined_models:
+                png_custom_model = metadata["Model"]
+            # If nothing had matched, check vendor/hf_model_id
+            if not png_custom_model and metadata["Model"].count("/"):
+                png_hf_model_id = metadata["Model"]
+            # No matching model was found
+            if not png_custom_model and not png_hf_model_id:
+                print(
+                    "Import PNG info: Unable to find a matching model for %s"
+                    % metadata["Model"]
+                )
+
+        outputs = {
+            png_info_img: None,
+            negative_prompt: metadata["Negative prompt"],
+            steps: int(metadata["Steps"]),
+            guidance_scale: float(metadata["CFG scale"]),
+            seed: int(metadata["Seed"]),
+            width: float(metadata["Size-1"]),
+            height: float(metadata["Size-2"]),
+        }
+        if "Model" in metadata and png_custom_model:
+            outputs[custom_model] = png_custom_model
+            outputs[hf_model_id] = ""
+        if "Model" in metadata and png_hf_model_id:
+            outputs[custom_model] = "None"
+            outputs[hf_model_id] = png_hf_model_id
+        if "Prompt" in metadata:
+            outputs[prompt] = metadata["Prompt"]
+        if "Sampler" in metadata:
+            if metadata["Sampler"] in scheduler_list_txt2img:
+                outputs[scheduler] = metadata["Sampler"]
+            else:
+                print(
+                    "Import PNG info: Unable to find a scheduler for %s"
+                    % metadata["Sampler"]
+                )
+
+        return outputs
+
+    except Exception as ex:
+        if pil_data and pil_data.info.get("parameters"):
+            print("import_png_metadata failed with %s" % ex)
+        pass
+
+    return {
+        png_info_img: None,
+    }

benchmarks/tests/test_benchmark.py

@@ -129,12 +129,12 @@ pytest_benchmark_param = pytest.mark.parametrize(
         pytest.param(True, "cpu", marks=pytest.mark.skip),
         pytest.param(
             False,
-            "cuda",
+            "gpu",
             marks=pytest.mark.skipif(
-                check_device_drivers("cuda"), reason="nvidia-smi not found"
+                check_device_drivers("gpu"), reason="nvidia-smi not found"
             ),
         ),
-        pytest.param(True, "cuda", marks=pytest.mark.skip),
+        pytest.param(True, "gpu", marks=pytest.mark.skip),
         pytest.param(
             False,
             "vulkan",

build_tools/docker/Dockerfile-ubuntu-22.04

@@ -1,88 +0,0 @@
-ARG IMAGE_NAME
-FROM ${IMAGE_NAME}:12.2.0-runtime-ubuntu22.04 as base
-
-ENV NV_CUDA_LIB_VERSION "12.2.0-1"
-
-FROM base as base-amd64
-
-ENV NV_CUDA_CUDART_DEV_VERSION 12.2.53-1
-ENV NV_NVML_DEV_VERSION 12.2.81-1
-ENV NV_LIBCUSPARSE_DEV_VERSION 12.1.1.53-1
-ENV NV_LIBNPP_DEV_VERSION 12.1.1.14-1
-ENV NV_LIBNPP_DEV_PACKAGE libnpp-dev-12-2=${NV_LIBNPP_DEV_VERSION}
-
-ENV NV_LIBCUBLAS_DEV_VERSION 12.2.1.16-1
-ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME libcublas-dev-12-2
-ENV NV_LIBCUBLAS_DEV_PACKAGE ${NV_LIBCUBLAS_DEV_PACKAGE_NAME}=${NV_LIBCUBLAS_DEV_VERSION}
-
-ENV NV_CUDA_NSIGHT_COMPUTE_VERSION 12.2.0-1
-ENV NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE cuda-nsight-compute-12-2=${NV_CUDA_NSIGHT_COMPUTE_VERSION}
-
-ENV NV_NVPROF_VERSION 12.2.60-1
-ENV NV_NVPROF_DEV_PACKAGE cuda-nvprof-12-2=${NV_NVPROF_VERSION}
-FROM base as base-arm64
-
-ENV NV_CUDA_CUDART_DEV_VERSION 12.2.53-1
-ENV NV_NVML_DEV_VERSION 12.2.81-1
-ENV NV_LIBCUSPARSE_DEV_VERSION 12.1.1.53-1
-ENV NV_LIBNPP_DEV_VERSION 12.1.1.14-1
-ENV NV_LIBNPP_DEV_PACKAGE libnpp-dev-12-2=${NV_LIBNPP_DEV_VERSION}
-
-ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME libcublas-dev-12-2
-ENV NV_LIBCUBLAS_DEV_VERSION 12.2.1.16-1
-ENV NV_LIBCUBLAS_DEV_PACKAGE ${NV_LIBCUBLAS_DEV_PACKAGE_NAME}=${NV_LIBCUBLAS_DEV_VERSION}
-
-ENV NV_CUDA_NSIGHT_COMPUTE_VERSION 12.2.0-1
-ENV NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE cuda-nsight-compute-12-2=${NV_CUDA_NSIGHT_COMPUTE_VERSION}
-
-FROM base-${TARGETARCH}
-
-ARG TARGETARCH
-
-LABEL maintainer "SHARK<stdin@nod.com>"
-
-# Register the ROCM package repository, and install rocm-dev package
-ARG ROCM_VERSION=5.6
-ARG AMDGPU_VERSION=5.6
-
-ARG APT_PREF
-RUN echo "$APT_PREF" > /etc/apt/preferences.d/rocm-pin-600
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends ca-certificates curl libnuma-dev gnupg \
-  && curl -sL https://repo.radeon.com/rocm/rocm.gpg.key | apt-key add - \
-  && printf "deb [arch=amd64] https://repo.radeon.com/rocm/apt/$ROCM_VERSION/ jammy main" | tee /etc/apt/sources.list.d/rocm.list \
-  && printf "deb [arch=amd64] https://repo.radeon.com/amdgpu/$AMDGPU_VERSION/ubuntu jammy main" | tee /etc/apt/sources.list.d/amdgpu.list \
-  && apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-  sudo \
-  libelf1 \
-  kmod \
-  file \
-  python3 \
-  python3-pip \
-  rocm-dev \
-  rocm-libs \
-  rocm-hip-libraries \
-  build-essential && \
-  apt-get clean && \
-  rm -rf /var/lib/apt/lists/*
-
-RUN  groupadd -g 109 render
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    cuda-cudart-dev-12-2=${NV_CUDA_CUDART_DEV_VERSION} \
-    cuda-command-line-tools-12-2=${NV_CUDA_LIB_VERSION} \
-    cuda-minimal-build-12-2=${NV_CUDA_LIB_VERSION} \
-    cuda-libraries-dev-12-2=${NV_CUDA_LIB_VERSION} \
-    cuda-nvml-dev-12-2=${NV_NVML_DEV_VERSION} \
-    ${NV_NVPROF_DEV_PACKAGE} \
-    ${NV_LIBNPP_DEV_PACKAGE} \
-    libcusparse-dev-12-2=${NV_LIBCUSPARSE_DEV_VERSION} \
-    ${NV_LIBCUBLAS_DEV_PACKAGE} \
-    ${NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE} \
-    && rm -rf /var/lib/apt/lists/*
-
-RUN apt install rocm-hip-libraries
-
-# Keep apt from auto upgrading the cublas and nccl packages. See https://gitlab.com/nvidia/container-images/cuda/-/issues/88
-RUN apt-mark hold ${NV_LIBCUBLAS_DEV_PACKAGE_NAME}
-ENV LIBRARY_PATH /usr/local/cuda/lib64/stubs
-

build_tools/docker/README.md

@@ -1,41 +0,0 @@
-On your host install your Nvidia or AMD gpu drivers. 
-
-**HOST Setup**
-
-*Ubuntu 23.04 Nvidia*
-```
-sudo ubuntu-drivers install
-```
-
-Install [docker](https://docs.docker.com/engine/install/ubuntu/) and the post-install to run as a [user](https://docs.docker.com/engine/install/linux-postinstall/)
-
-Install Nvidia [Container and register it](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html). In Ubuntu 23.04 systems follow [this](https://github.com/NVIDIA/nvidia-container-toolkit/issues/72#issuecomment-1584574298)
-
-
-Build docker with :
-
-```
-docker build . -f Dockerfile-ubuntu-22.04 -t shark/dev-22.04:5.6 --build-arg=ROCM_VERSION=5.6 --build-arg=AMDGPU_VERSION=5.6 --build-arg=APT_PREF="Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600" --build-arg=IMAGE_NAME=nvidia/cuda --build-arg=TARGETARCH=amd64
-```
-
-Run with:
-
-*CPU*
-
-```
-docker run  -it docker.io/shark/dev-22.04:5.6
-```
-
-*Nvidia GPU*
-
-```
-docker run --rm -it --gpus all docker.io/shark/dev-22.04:5.6
-```
-
-*AMD GPUs*
-
-```
-docker run --device /dev/kfd --device /dev/dri  docker.io/shark/dev-22.04:5.6
-```
-
-More AMD instructions are [here](https://docs.amd.com/en/latest/deploy/docker.html)

build_tools/image_comparison.py

@@ -24,13 +24,13 @@ def get_image(url, local_filename):
             shutil.copyfileobj(res.raw, f)
 
 
-def compare_images(new_filename, golden_filename, upload=False):
+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" and upload == True:
+        if os.name != "nt":
             subprocess.run(
                 [
                     "gsutil",
@@ -39,7 +39,7 @@ def compare_images(new_filename, golden_filename, upload=False):
                     "gs://shark_tank/testdata/builder/",
                 ]
             )
-        raise AssertionError("new and golden not close")
+        raise SystemExit("new and golden not close")
     else:
         print("SUCCESS")
 

build_tools/populate_sharktank_ci.sh

@@ -1,6 +1,5 @@
 #!/bin/bash
 
-IMPORTER=1 BENCHMARK=1 NO_BREVITAS=1 ./setup_venv.sh
+IMPORTER=1 BENCHMARK=1 ./setup_venv.sh
 source $GITHUB_WORKSPACE/shark.venv/bin/activate
-python build_tools/stable_diffusion_testing.py --gen
-python tank/generate_sharktank.py
+python generate_sharktank.py

build_tools/stable_diffusion_testing.py

@@ -36,7 +36,9 @@ def parse_sd_out(filename, command, device, use_tune, model_name, import_mlir):
                 metrics[val] = line.split(" ")[-1].strip("\n")
 
     metrics["Average step"] = metrics["Average step"].strip("ms/it")
-    metrics["Total image generation"] = metrics["Total image generation"].strip("sec")
+    metrics["Total image generation"] = metrics[
+        "Total image generation"
+    ].strip("sec")
     metrics["device"] = device
     metrics["use_tune"] = use_tune
     metrics["model_name"] = model_name
@@ -61,14 +63,7 @@ def get_inpaint_inputs():
     open("./test_images/inputs/mask.png", "wb").write(mask.content)
 
 
-def test_loop(
-    device="vulkan",
-    beta=False,
-    extra_flags=[],
-    upload_bool=True,
-    exit_on_fail=True,
-    do_gen=False,
-):
+def test_loop(device="vulkan", beta=False, extra_flags=[]):
     # Get golden values from tank
     shutil.rmtree("./test_images", ignore_errors=True)
     model_metrics = []
@@ -76,49 +71,27 @@ def test_loop(
     os.mkdir("./test_images/golden")
     get_inpaint_inputs()
     hf_model_names = model_config_dicts[0].values()
-    tuned_options = [
-        "--no-use_tuned",
-        "--use_tuned",
-    ]
+    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"
-    )
+    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"'
-        )
+        inpaint_prompt_text = '--prompt="Face of a yellow cat, high resolution, sitting on a park bench"'
     if beta:
         extra_flags.append("--beta_models=True")
     extra_flags.append("--no-progress_bar")
-    if do_gen:
-        extra_flags.append("--import_debug")
     to_skip = [
         "Linaqruf/anything-v3.0",
         "prompthero/openjourney",
         "wavymulder/Analog-Diffusion",
         "dreamlike-art/dreamlike-diffusion-1.0",
     ]
-    counter = 0
     for import_opt in import_options:
         for model_name in hf_model_names:
             if model_name in to_skip:
                 continue
             for use_tune in tuned_options:
-                if (
-                    model_name == "stabilityai/stable-diffusion-2-1"
-                    and use_tune == tuned_options[0]
-                ):
-                    continue
-                elif (
-                    model_name == "stabilityai/stable-diffusion-2-1-base"
-                    and use_tune == tuned_options[1]
-                ):
-                    continue
-                elif use_tune == tuned_options[1]:
-                    continue
                 command = (
                     [
                         executable,  # executable is the python from the venv used to run this
@@ -176,7 +149,9 @@ def test_loop(
                     )
                     print(command)
                     print("Successfully generated image")
-                    os.makedirs("./test_images/golden/" + model_name, exist_ok=True)
+                    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,
@@ -191,35 +166,17 @@ def test_loop(
                     )
                     test_file = glob(test_file_path)[0]
 
-                    golden_path = "./test_images/golden/" + model_name + "/*.png"
+                    golden_path = (
+                        "./test_images/golden/" + model_name + "/*.png"
+                    )
                     golden_file = glob(golden_path)[0]
-                    try:
-                        compare_images(test_file, golden_file, upload=upload_bool)
-                    except AssertionError as e:
-                        print(e)
-                        if exit_on_fail == True:
-                            raise
+                    compare_images(test_file, golden_file)
                 else:
                     print(command)
                     print("failed to generate image for this configuration")
-                    with open(dumpfile_name, "r+") as f:
-                        output = f.readlines()
-                        print("\n".join(output))
-                    exit(1)
-                if os.name == "nt":
-                    counter += 1
-                    if counter % 2 == 0:
-                        extra_flags.append(
-                            "--iree_vulkan_target_triple=rdna2-unknown-windows"
-                        )
-                    else:
-                        if counter != 1:
-                            extra_flags.remove(
-                                "--iree_vulkan_target_triple=rdna2-unknown-windows"
-                            )
-            if do_gen:
-                prepare_artifacts()
-
+                    if "2_1_base" in model_name:
+                        print("failed a known successful model.")
+                        exit(1)
     with open(os.path.join(os.getcwd(), "sd_testing_metrics.csv"), "w+") as f:
         header = "model_name;device;use_tune;import_opt;Clip Inference time(ms);Average Step (ms/it);VAE Inference time(ms);total image generation(s);command\n"
         f.write(header)
@@ -238,47 +195,15 @@ def test_loop(
             f.write(";".join(output) + "\n")
 
 
-def prepare_artifacts():
-    gen_path = os.path.join(os.getcwd(), "gen_shark_tank")
-    if not os.path.isdir(gen_path):
-        os.mkdir(gen_path)
-    for dirname in os.listdir(os.getcwd()):
-        for modelname in ["clip", "unet", "vae"]:
-            if modelname in dirname and "vmfb" not in dirname:
-                if not os.path.isdir(os.path.join(gen_path, dirname)):
-                    shutil.move(os.path.join(os.getcwd(), dirname), gen_path)
-                    print(f"Moved dir: {dirname} to {gen_path}.")
-
-
 parser = argparse.ArgumentParser()
 
 parser.add_argument("-d", "--device", default="vulkan")
 parser.add_argument(
     "-b", "--beta", action=argparse.BooleanOptionalAction, default=False
 )
-parser.add_argument("-e", "--extra_args", type=str, default=None)
-parser.add_argument(
-    "-u", "--upload", action=argparse.BooleanOptionalAction, default=True
-)
-parser.add_argument(
-    "-x", "--exit_on_fail", action=argparse.BooleanOptionalAction, default=True
-)
-parser.add_argument("-g", "--gen", action=argparse.BooleanOptionalAction, default=False)
+
 
 if __name__ == "__main__":
     args = parser.parse_args()
     print(args)
-    extra_args = []
-    if args.extra_args:
-        for arg in args.extra_args.split(","):
-            extra_args.append(arg)
-    test_loop(
-        args.device,
-        args.beta,
-        extra_args,
-        args.upload,
-        args.exit_on_fail,
-        args.gen,
-    )
-    if args.gen:
-        prepare_artifacts()
+    test_loop(args.device, args.beta, [])

build_tools/vicuna_testing.py

@@ -1,14 +0,0 @@
-import os
-from sys import executable
-import subprocess
-from apps.language_models.scripts import vicuna
-
-
-def test_loop():
-    precisions = ["fp16", "int8", "int4"]
-    devices = ["cpu"]
-    for precision in precisions:
-        for device in devices:
-            model = vicuna.UnshardedVicuna(device=device, precision=precision)
-            model.compile()
-            del model

conftest.py

@@ -2,11 +2,9 @@ def pytest_addoption(parser):
     # Attaches SHARK command-line arguments to the pytest machinery.
     parser.addoption(
         "--benchmark",
-        action="store",
-        type=str,
-        default=None,
-        choices=("baseline", "native", "all"),
-        help="Benchmarks specified engine(s) and writes bench_results.csv.",
+        action="store_true",
+        default="False",
+        help="Pass option to benchmark and write results.csv",
     )
     parser.addoption(
         "--onnx_bench",
@@ -42,13 +40,7 @@ def pytest_addoption(parser):
         "--update_tank",
         action="store_true",
         default="False",
-        help="Update local shark tank with latest artifacts if model artifact hash mismatched.",
-    )
-    parser.addoption(
-        "--force_update_tank",
-        action="store_true",
-        default="False",
-        help="Force-update local shark tank with artifacts from specified shark_tank URL (defaults to nightly).",
+        help="Update local shark tank with latest artifacts.",
     )
     parser.addoption(
         "--ci_sha",
@@ -59,21 +51,15 @@ def pytest_addoption(parser):
     parser.addoption(
         "--local_tank_cache",
         action="store",
-        default=None,
+        default="",
         help="Specify the directory in which all downloaded shark_tank artifacts will be cached.",
     )
     parser.addoption(
         "--tank_url",
         type=str,
-        default="gs://shark_tank/nightly",
+        default="gs://shark_tank/latest",
         help="URL to bucket from which to download SHARK tank artifacts. Default is gs://shark_tank/latest",
     )
-    parser.addoption(
-        "--tank_prefix",
-        type=str,
-        default=None,
-        help="Prefix to gs://shark_tank/ model directories from which to download SHARK tank artifacts. Default is nightly.",
-    )
     parser.addoption(
         "--benchmark_dispatches",
         default=None,
@@ -84,9 +70,3 @@ def pytest_addoption(parser):
         default="./temp_dispatch_benchmarks",
         help="Directory in which dispatch benchmarks are saved.",
     )
-    parser.addoption(
-        "--batchsize",
-        default=1,
-        type=int,
-        help="Batch size for the tested model.",
-    )

cpp/CMakeLists.txt

@@ -27,7 +27,7 @@ include(FetchContent)
 
 FetchContent_Declare(
   iree
-  GIT_REPOSITORY https://github.com/nod-ai/srt.git
+  GIT_REPOSITORY https://github.com/nod-ai/shark-runtime.git
   GIT_TAG shark 
   GIT_SUBMODULES_RECURSE OFF
   GIT_SHALLOW OFF

cpp/README.md

@@ -40,7 +40,7 @@ cmake --build build/
 *Prepare the model*
 ```bash
 wget https://storage.googleapis.com/shark_tank/latest/resnet50_tf/resnet50_tf.mlir
-iree-compile --iree-input-type=auto --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --iree-llvmcpu-embedded-linker-path=`python3 -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])'`/iree/compiler/tools/../_mlir_libs/iree-lld --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --mlir-pass-pipeline-crash-reproducer=ist/core-reproducer.mlir --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux  resnet50_tf.mlir -o resnet50_tf.vmfb
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --iree-llvmcpu-embedded-linker-path=`python3 -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])'`/iree/compiler/tools/../_mlir_libs/iree-lld --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --mlir-pass-pipeline-crash-reproducer=ist/core-reproducer.mlir --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 resnet50_tf.mlir -o resnet50_tf.vmfb
 ```
 *Prepare the input*
 
@@ -65,18 +65,18 @@ A tool for benchmarking other models is built and can be invoked with a command
 see `./build/vulkan_gui/iree-vulkan-gui --help` for an explanation on the function input. For example, stable diffusion unet can be tested with the following commands:
 ```bash
 wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/stable_diff_tf.mlir
-iree-compile --iree-input-type=auto --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux  stable_diff_tf.mlir -o stable_diff_tf.vmfb
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 stable_diff_tf.mlir -o stable_diff_tf.vmfb
 ./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=2x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32
 ```
 VAE and Autoencoder are also available
 ```bash
 # VAE
 wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/vae_tf/vae.mlir
-iree-compile --iree-input-type=auto --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux  vae.mlir -o vae.vmfb
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 vae.mlir -o vae.vmfb
 ./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=1x4x64x64xf32
 
 # CLIP Autoencoder
 wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/clip_tf/clip_autoencoder.mlir
-iree-compile --iree-input-type=auto --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux  clip_autoencoder.mlir -o clip_autoencoder.vmfb
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 clip_autoencoder.mlir -o clip_autoencoder.vmfb
 ./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=1x77xi32 --function_input=1x77xi32
 ```

cpp/vision_inference/CMakeLists.txt

@@ -21,7 +21,7 @@ endif()
 # Compile mnist.mlir to mnist.vmfb.
 set(_COMPILE_TOOL_EXECUTABLE $<TARGET_FILE:iree-compile>)
 set(_COMPILE_ARGS)
-list(APPEND _COMPILE_ARGS "--iree-input-type=auto")
+list(APPEND _COMPILE_ARGS "--iree-input-type=mhlo")
 list(APPEND _COMPILE_ARGS "--iree-hal-target-backends=llvm-cpu")
 list(APPEND _COMPILE_ARGS "${IREE_SOURCE_DIR}/samples/models/mnist.mlir")
 list(APPEND _COMPILE_ARGS "-o")

dataset/annotation_tool.py

@@ -10,7 +10,9 @@ 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")
+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:
@@ -21,11 +23,8 @@ with gr.Blocks(title="Dataset Annotation Tool", css=demo_css) as shark_web:
                 value=nod_logo,
                 show_label=False,
                 interactive=False,
-                show_download_button=False,
                 elem_id="top_logo",
-                width=150,
-                height=100,
-            )
+            ).style(width=150, height=100)
 
     datasets, images, ds_w_prompts = get_datasets(args.gs_url)
     prompt_data = dict()
@@ -38,7 +37,7 @@ with gr.Blocks(title="Dataset Annotation Tool", css=demo_css) as shark_web:
     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", height=512)
+            image = gr.Image(type="filepath").style(height=512)
 
         with gr.Column(scale=1, min_width=600):
             prompts = gr.Dropdown(
@@ -74,7 +73,9 @@ with gr.Blocks(title="Dataset Annotation Tool", css=demo_css) as shark_web:
             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"]
+                        [line["text"]]
+                        if type(line["text"]) is str
+                        else line["text"]
                     )
 
         return gr.Dropdown.update(choices=images[dataset])
@@ -100,7 +101,9 @@ with gr.Blocks(title="Dataset Annotation Tool", css=demo_css) as shark_web:
             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)
+        return gr.Image.update(value=img), gr.Dropdown.update(
+            choices=prompt_choices
+        )
 
     image_name.change(
         fn=display_image,
@@ -117,7 +120,12 @@ with gr.Blocks(title="Dataset Annotation Tool", css=demo_css) as shark_web:
     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:
+        if (
+            dataset is None
+            or image_name is None
+            or prompts is None
+            or prompt is None
+        ):
             return
 
         if prompts == "Add new":
@@ -126,7 +134,9 @@ with gr.Blocks(title="Dataset Annotation Tool", css=demo_css) as shark_web:
             idx = prompt_data[image_name].index(prompts)
             prompt_data[image_name][idx] = prompt
 
-        prompt_path = str(shark_root) + "/dataset/" + dataset + "/metadata.jsonl"
+        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():
@@ -153,7 +163,9 @@ with gr.Blocks(title="Dataset Annotation Tool", css=demo_css) as shark_web:
             return
 
         prompt_data[image_name].remove(prompts)
-        prompt_path = str(shark_root) + "/dataset/" + dataset + "/metadata.jsonl"
+        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():
@@ -216,7 +228,9 @@ with gr.Blocks(title="Dataset Annotation Tool", css=demo_css) as shark_web:
         # 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'gsutil cp "{dataset_path}/metadata.jsonl" "{dataset_gs_path}"'
+        )
         os.system(f'rm -rf "{dataset_path}"')
 
         return gr.Dropdown.update(value=None)

dataset/requirements.txt

@@ -1,3 +1,3 @@
 # SHARK Annotator
-gradio==3.34.0
+gradio==3.15.0
 jsonlines

docs/shark_iree_profiling.md

@@ -55,7 +55,7 @@ The command line for compilation will start something like this, where the `-` n
 The `-o output_filename.vmfb` flag can be used to specify the location to save the compiled vmfb. Note that a dump of the
 dispatches that can be compiled + run in isolation can be generated by adding `--iree-hal-dump-executable-benchmarks-to=/some/directory`. Say, if they are in the `benchmarks` directory, the following compile/run commands would work for Vulkan on RDNA3.
 ```
-iree-compile --iree-input-type=none --iree-hal-target-backends=vulkan --iree-vulkan-target-triple=rdna3-unknown-linux  benchmarks/module_forward_dispatch_${NUM}_vulkan_spirv_fb.mlir -o benchmarks/module_forward_dispatch_${NUM}_vulkan_spirv_fb.vmfb
+iree-compile --iree-input-type=none --iree-hal-target-backends=vulkan --iree-vulkan-target-triple=rdna3-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 benchmarks/module_forward_dispatch_${NUM}_vulkan_spirv_fb.mlir -o benchmarks/module_forward_dispatch_${NUM}_vulkan_spirv_fb.vmfb
 
 iree-benchmark-module --module=benchmarks/module_forward_dispatch_${NUM}_vulkan_spirv_fb.vmfb --function=forward --device=vulkan
 ```
@@ -63,8 +63,8 @@ Where `${NUM}` is the dispatch number that you want to benchmark/profile in isol
 
 ### Enabling Tracy for Vulkan profiling
 
-To begin profiling with Tracy, a build of IREE runtime with tracing enabled is needed. SHARK-Runtime (SRT) builds an
-instrumented version alongside the normal version nightly (.whls typically found [here](https://github.com/nod-ai/SRT/releases)), however this is only available for Linux. For Windows, tracing can be enabled by enabling a CMake flag.
+To begin profiling with Tracy, a build of IREE runtime with tracing enabled is needed. SHARK-Runtime builds an
+instrumented version alongside the normal version nightly (.whls typically found [here](https://github.com/nod-ai/SHARK-Runtime/releases)), however this is only available for Linux. For Windows, tracing can be enabled by enabling a CMake flag.
 ```
 $env:IREE_ENABLE_RUNTIME_TRACING="ON"
 ```

docs/shark_sd_blender.md

@@ -1,75 +0,0 @@
-# Overview
-
-This document is intended to provide a starting point for using SHARK stable diffusion with Blender. 
-
-We currently make use of the [AI-Render Plugin](https://github.com/benrugg/AI-Render) to integrate with Blender.
-
-## Setup SHARK and prerequisites:
-
- * Download the latest SHARK SD webui .exe from [here](https://github.com/nod-ai/SHARK/releases) or follow instructions on the [README](https://github.com/nod-ai/SHARK#readme)
- * Once you have the .exe where you would like SHARK to install, run the .exe from terminal/PowerShell with the `--api` flag:
-```
-## Run the .exe in API mode:
-.\shark_sd_<date>_<ver>.exe --api
-
-## For example:
-.\shark_sd_20230411_671.exe --api --server_port=8082
-
-## From a the base directory of a source clone of SHARK:
-./setup_venv.ps1
-python apps\stable_diffusion\web\index.py --api
-
-```
-
-Your local SD server should start and look something like this:
-![image](https://user-images.githubusercontent.com/87458719/231369758-e2c3c45a-eccc-4fe5-a788-4a3bf1ace1d1.png)
-
- * Note: When running in api mode with `--api`, the .exe will not function as a webUI. Thus, the address in the terminal output will only be useful for API requests.
-
-### Install AI Render
-
-- Get AI Render on [Blender Market](https://blendermarket.com/products/ai-render) or [Gumroad](https://airender.gumroad.com/l/ai-render)
-- Open Blender, then go to Edit > Preferences > Add-ons > Install and then find the zip file
-- We will be using the Automatic1111 SD backend for the AI-Render plugin. Follow instructions [here](https://github.com/benrugg/AI-Render/wiki/Local-Installation) to setup local SD backend.
-
-Your AI-Render preferences should be configured as shown; the highlighted part should match your terminal output:
-![image](https://user-images.githubusercontent.com/87458719/231390322-59a54a09-520a-4a08-b658-6e37bd63e932.png)
-
-
-The [AI-Render README](https://github.com/benrugg/AI-Render/blob/main/README.md) has more details on installation and usage, as well as video tutorials.
-
-## Using AI-Render + SHARK in your Blender project
-
-- In the Render Properties tab, in the AI-Render dropdown, enable AI-Render.
-
-![image](https://user-images.githubusercontent.com/87458719/231392843-9bd51744-3ce2-464e-843a-0c4d4c96df0c.png)
-
-- Select an image size (it's usually better to upscale later than go high on the img2img resolution here.)
-
-![image](https://user-images.githubusercontent.com/87458719/231394288-0c4ab8c5-dc30-4dbe-8bc1-7520ded5efe8.png)
-
-- From here, you can enter a prompt and configure img2img Stable Diffusion parameters, and AI-Render will run SHARK SD img2img on the rendered scene.
-- AI-Render has useful presets for aesthetic styles, so you should be able to keep your subject prompt simple and focus on creating a decent Blender scene to start from.
-
-![image](https://user-images.githubusercontent.com/87458719/231440729-2fe69586-41cb-4274-9ce7-f6c08def600b.png)
-
-## Examples:
-Scene (Input image):
-
-![blender-sample-2](https://user-images.githubusercontent.com/87458719/231450408-0e680086-3e52-4962-a5c1-c703a94d1583.png)
-
-Prompt:
-"A bowl of tangerines in front of rocks, masterpiece, oil on canvas, by Georgia O'Keefe, trending on artstation, landscape painting by Caspar David Friedrich"
-
-Negative Prompt (default):
-"ugly, bad art, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, blurry, bad anatomy, blurred, watermark, grainy, tiling, signature, cut off, draft"
-
-Example output:
-
-![blender-sample-2_out](https://user-images.githubusercontent.com/87458719/231451145-a0b56897-a7d0-4add-bbed-7e8af21a65df.png)
-
-
-
-
-
-

docs/shark_sd_koboldcpp.md

@@ -1,140 +0,0 @@
-# Overview
-
-In [1.47.2](https://github.com/LostRuins/koboldcpp/releases/tag/v1.47.2) [Koboldcpp](https://github.com/LostRuins/koboldcpp) added AUTOMATIC1111 integration for image generation. Since SHARK implements a small subset of the A1111 REST api, you can also use SHARK for this. This document gives a starting point for how to get this working.
-
-## In Action
-
-![preview](https://user-images.githubusercontent.com/121311569/280557602-bb97bad0-fdf5-4922-a2cc-4f327f2760db.jpg)
-
-## Memory considerations
-
-Since both Koboldcpp and SHARK will use VRAM on your graphic card(s) running both at the same time using the same card will impose extra limitations on the model size you can fully offload to the video card in Koboldcpp. For me, on a RX 7900 XTX on Windows with 24 GiB of VRAM, the limit was about a 13 Billion parameter model with Q5_K_M quantisation.
-
-## Performance Considerations
-
-When using SHARK for image generation, especially with Koboldcpp, you need to be aware that it is currently designed to pay a large upfront cost in time compiling and tuning the model you select, to get an optimal individual image generation time. You need to be the judge as to whether this trade-off is going to be worth it for your OS and hardware combination.
-
-It means that the first time you run a particular Stable Diffusion model for a particular combination of image size, LoRA, and VAE, SHARK will spend *many minutes* - even on a beefy machaine with very fast graphics card with lots of memory - building that model combination just so it can save it to disk. It may even have to go away and download the model if it doesn't already have it locally. Once it has done its build of a model combination for your hardware once, it shouldn't need to do it again until you upgrade to a newer SHARK version, install different drivers or change your graphics hardware. It will just upload the files it generated the first time to your graphics card and proceed from there.
-
-This does mean however, that on a brand new fresh install of SHARK that has not generated any images on a model you haven't selected before, the first image Koboldcpp requests may look like it is *never* going finish and that the whole process has broken. Be forewarned, make yourself a cup of coffee, and expect a lot of messages about compilation and tuning from SHARK in the terminal you ran it from.
-
-## Setup SHARK and prerequisites:
-
- * Make sure you have suitable drivers for your graphics card installed. See the prerequisties section of the [README](https://github.com/nod-ai/SHARK#readme).
- * Download the latest SHARK studio .exe from [here](https://github.com/nod-ai/SHARK/releases) or follow the instructions in the [README](https://github.com/nod-ai/SHARK#readme) for an advanced, Linux or Mac install.
- * Run SHARK from terminal/PowerShell with the `--api` flag. Since koboldcpp also expects both CORS support and the image generator to be running on port `7860` rather than SHARK default of `8080`, also include both the `--api_accept_origin` flag with a suitable origin (use `="*"` to enable all origins) and `--server_port=7860` on the command line. (See the if you want to run SHARK on a different port)
-
-```powershell
-## Run the .exe in API mode, with CORS support, on the A1111 endpoint port:
-.\node_ai_shark_studio_<date>_<ver>.exe --api --api_accept_origin="*"  --server_port=7860
-
-## Run trom the base directory of a source clone of SHARK on Windows:
-.\setup_venv.ps1
-python .\apps\stable_diffusion\web\index.py --api --api_accept_origin="*"  --server_port=7860
-
-## Run a the base directory of a source clone of SHARK on Linux:
-./setup_venv.sh
-source shark.venv/bin/activate
-python ./apps/stable_diffusion/web/index.py --api --api_accept_origin="*"  --server_port=7860
-
-## An example giving improved performance on AMD cards using vulkan, that runs on the same port as A1111
-.\node_ai_shark_studio_20320901_2525.exe --api --api_accept_origin="*" --device_allocator="caching" --server_port=7860
-
-## Since the api respects most applicable SHARK command line arguments for options not specified,
-## or currently unimplemented by API, there might be some you want to set, as listed in `--help`
-.\node_ai_shark_studio_20320901_2525.exe --help
-
-## For instance, the example above, but with a a custom VAE specified
-.\node_ai_shark_studio_20320901_2525.exe --api --api_accept_origin="*" --device_allocator="caching" --server_port=7860 --custom_vae="clearvae_v23.safetensors"
-
-## An example with multiple specific CORS origins
-python apps/stable_diffusion/web/index.py --api --api_accept_origin="koboldcpp.example.com:7001" --api_accept_origin="koboldcpp.example.com:7002" --server_port=7860
-```
-
-SHARK should start in server mode, and you should see something like this:
-
-![SHARK API startup](https://user-images.githubusercontent.com/121311569/280556294-c3f7fc1a-c8e2-467d-afe6-365638d6823a.png)
-
-* Note: When running in api mode with `--api`, the .exe will not function as a webUI. Thus, the address or port shown in the terminal output will only be useful for API requests.
-
-
-## Configure Koboldcpp for local image generation:
-
-* Get the latest [Koboldcpp](https://github.com/LostRuins/koboldcpp/releases) if you don't already have it. If you have a recent AMD card that has ROCm HIP [support for Windows](https://rocmdocs.amd.com/en/latest/release/windows_support.html#windows-supported-gpus) or [support for Linux](https://rocmdocs.amd.com/en/latest/release/gpu_os_support.html#linux-supported-gpus), you'll likely prefer [YellowRosecx's ROCm fork](https://github.com/YellowRoseCx/koboldcpp-rocm).
-* Start Koboldcpp in another terminal/Powershell and setup your model configuration. Refer to the [Koboldcpp README](https://github.com/YellowRoseCx/koboldcpp-rocm) for more details on how to do this if this is your first time using Koboldcpp.
-* Once the main UI has loaded into your browser click the settings button, go to the advanced tab, and then choose *Local A1111* from the generate images dropdown:
-
-  ![Settings button location](https://user-images.githubusercontent.com/121311569/280556246-10692d79-e89f-4fdf-87ba-82f3d78ed49d.png)
-
-  ![Advanced Settings with 'Local A1111' location](https://user-images.githubusercontent.com/121311569/280556234-6ebc8ba7-1469-442a-93a7-5626a094ddf1.png)
-
-  *if you get an error here, see the next section [below](#connecting-to-shark-on-a-different-address-or-port)*
-
-* A list of Stable Diffusion models available to your SHARK instance should now be listed in the box below *generate images*. The default value will usually be set to `stabilityai/stable-diffusion-2-1-base`. Choose the model you want to use for image generation from the list (but see [performance considerations](#performance-considerations)).
-* You should now be ready to generate images, either by clicking the 'Add Img' button above the text entry box:
-
-  ![Add Image Button](https://user-images.githubusercontent.com/121311569/280556161-846c7883-4a83-4458-a56a-bd9f93ca354c.png)
-
-  ...or by selecting the 'Autogenerate' option in the settings:
-
-  ![Setting the autogenerate images option](https://user-images.githubusercontent.com/121311569/280556230-ae221a46-ba68-499b-a519-c8f290bbbeae.png)
-
-  *I often find that even if I have selected autogenerate I have to do an 'add img' to get things started off*
-
-* There is one final piece of image generation configuration within Koboldcpp you might want to do. This is also in the generate images section of advanced settings. Here there is, not very obviously, a 'style' button:
-
-  ![Selecting the 'styles' button](https://user-images.githubusercontent.com/121311569/280556694-55cd1c55-a059-4b54-9293-63d66a32368e.png)
-
-  This will bring up a dialog box where you can enter a short text that will sent as a prefix to the Prompt sent to SHARK:
-
-  ![Entering extra image styles](https://user-images.githubusercontent.com/121311569/280556172-4aab9794-7a77-46d7-bdda-43df570ad19a.png)
-
-
-## Connecting to SHARK on a different address or port
-
-If you didn't set the port to `--server_port=7860` when starting SHARK, or you are running it on different machine on your network than you are running Koboldcpp, or to where you are running the koboldcpp's kdlite client frontend, then you very likely got the following error:
-
-  ![Can't find the A1111 endpoint error](https://user-images.githubusercontent.com/121311569/280555857-601f53dc-35e9-4027-9180-baa61d2393ba.png)
-
-As long as SHARK is running correctly, this means you need to set the url and port to the correct values in Koboldcpp. For instance. to set the port that Koboldcpp looks for an image generator to SHARK's default port of 8080:
-
-* Select the cog icon the Generate Images section of Advanced settings:
-
-     ![Selecting the endpoint cog](https://user-images.githubusercontent.com/121311569/280555866-4287ecc5-f29f-4c03-8f5a-abeaf31b0442.png)
-
-* Then edit the port number at the end of the url in the 'A1111 Endpoint Selection' dialog box to read 8080:
-
-     ![Changing the endpoint port](https://user-images.githubusercontent.com/121311569/280556170-f8848b7b-6fc9-4cf7-80eb-5c312f332fd9.png)
-
-* Similarly, when running SHARK on a different machine you will need to change host part of the endpoint url to the hostname or ip address where SHARK is running, similarly:
-
-    ![Changing the endpoint hostname](https://user-images.githubusercontent.com/121311569/280556167-c6541dea-0f85-417a-b661-fdf4dc40d05f.png)
-
-## Examples
-
-Here's how Koboldcpp shows an image being requested:
-
-  ![An image being generated]((https://user-images.githubusercontent.com/121311569/280556210-bb1c9efd-79ac-478e-b726-b25b82ef2186.png)
-
-The generated image in context in story mode:
-
- ![A generated image](https://user-images.githubusercontent.com/121311569/280556179-4e9f3752-f349-4cba-bc6a-f85f8dc79b10.jpg)
-
-And the same image when clicked on:
-
- ![A selected image](https://user-images.githubusercontent.com/121311569/280556216-2ca4c0a4-3889-4ef5-8a09-30084fb34081.jpg)
-
-
-## Where to find the images in SHARK
-
-Even though Koboldcpp requests images at a size of 512x512, it resizes then to 256x256, converts them to `.jpeg`, and only shows them at 200x200 in the main text window. It does this so it can save them compactly embedded in your story as a `data://` uri.
-
-However the images at the original size are saved by SHARK in its `output_dir` which is usually a folder named for the current date. inside `generated_imgs` folder in the SHARK installation directory.
-
-You can browse these, either using the Output Gallery tab from within the SHARK web ui:
-
-  ![SHARK web ui output gallery tab](https://user-images.githubusercontent.com/121311569/280556582-9303ca85-2594-4a8c-97a2-fbd72337980b.jpg)
-
-...or by browsing to the `output_dir` in your operating system's file manager:
-
-  ![SHARK output directory subfolder in Windows File Explorer](https://user-images.githubusercontent.com/121311569/280556297-66173030-2324-415c-a236-ef3fcd73e6ed.jpg)

generate_sharktank.py

@@ -0,0 +1,278 @@
+# Lint as: python3
+"""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:
+#   /SHARK
+#     /gen_shark_tank
+#       /albert_lite_base
+#       /...model_name...
+#
+
+import os
+import csv
+import argparse
+from shark.shark_importer import SharkImporter
+import subprocess as sp
+import hashlib
+import numpy as np
+from pathlib import Path
+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):
+    with open(file_name, "rb") as f:
+        file_hash = hashlib.blake2b()
+        while chunk := f.read(2**20):
+            file_hash.update(chunk)
+
+    return file_hash.hexdigest()
+
+
+def save_torch_model(torch_model_list):
+    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=",")
+        fields = next(torch_reader)
+        for row in torch_reader:
+            torch_model_name = row[0]
+            tracing_required = row[1]
+            model_type = row[2]
+            is_dynamic = row[3]
+
+            tracing_required = False if tracing_required == "False" else True
+            is_dynamic = False if is_dynamic == "False" else True
+            print("generating artifacts for: " + torch_model_name)
+            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"
+            )
+            os.makedirs(torch_model_dir, exist_ok=True)
+
+            mlir_importer = SharkImporter(
+                model,
+                (input,),
+                frontend="torch",
+            )
+            mlir_importer.import_debug(
+                is_dynamic=False,
+                tracing_required=tracing_required,
+                dir=torch_model_dir,
+                model_name=torch_model_name,
+            )
+            # Generate torch dynamic models.
+            if is_dynamic:
+                mlir_importer.import_debug(
+                    is_dynamic=True,
+                    tracing_required=tracing_required,
+                    dir=torch_model_dir,
+                    model_name=torch_model_name + "_dynamic",
+                )
+
+
+def save_tf_model(tf_model_list):
+    from tank.model_utils_tf import (
+        get_causal_image_model,
+        get_causal_lm_model,
+        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=",")
+        fields = next(tf_reader)
+        for row in tf_reader:
+            tf_model_name = row[0]
+            model_type = row[1]
+
+            model = None
+            input = None
+            print(f"Generating artifacts for model {tf_model_name}")
+            if model_type == "hf":
+                model, input, _ = get_causal_lm_model(tf_model_name)
+            if model_type == "img":
+                model, input, _ = get_causal_image_model(tf_model_name)
+            if model_type == "keras":
+                model, input, _ = get_keras_model(tf_model_name)
+            if model_type == "TFhf":
+                model, input, _ = get_TFhf_model(tf_model_name)
+
+            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,
+                inputs=input,
+                frontend="tf",
+            )
+            mlir_importer.import_debug(
+                is_dynamic=False,
+                dir=tf_model_dir,
+                model_name=tf_model_name,
+            )
+            mlir_hash = create_hash(
+                os.path.join(tf_model_dir, tf_model_name + "_tf" + ".mlir")
+            )
+            np.save(os.path.join(tf_model_dir, "hash"), np.array(mlir_hash))
+
+
+def save_tflite_model(tflite_model_list):
+    from shark.tflite_utils import TFLitePreprocessor
+
+    with open(tflite_model_list) as csvfile:
+        tflite_reader = csv.reader(csvfile, delimiter=",")
+        for row in tflite_reader:
+            print("\n")
+            tflite_model_name = row[0]
+            tflite_model_link = row[1]
+            print("tflite_model_name", tflite_model_name)
+            print("tflite_model_link", tflite_model_link)
+            tflite_model_name_dir = os.path.join(
+                WORKDIR, str(tflite_model_name) + "_tflite"
+            )
+            os.makedirs(tflite_model_name_dir, exist_ok=True)
+            print(f"TMP_TFLITE_MODELNAME_DIR = {tflite_model_name_dir}")
+
+            # Preprocess to get SharkImporter input args
+            tflite_preprocessor = TFLitePreprocessor(str(tflite_model_name))
+            raw_model_file_path = tflite_preprocessor.get_raw_model_file()
+            inputs = tflite_preprocessor.get_inputs()
+            tflite_interpreter = tflite_preprocessor.get_interpreter()
+
+            # Use SharkImporter to get SharkInference input args
+            my_shark_importer = SharkImporter(
+                module=tflite_interpreter,
+                inputs=inputs,
+                frontend="tflite",
+                raw_model_file=raw_model_file_path,
+            )
+            my_shark_importer.import_debug(
+                dir=tflite_model_name_dir,
+                model_name=tflite_model_name,
+                func_name="main",
+            )
+            mlir_hash = create_hash(
+                os.path.join(
+                    tflite_model_name_dir,
+                    tflite_model_name + "_tflite" + ".mlir",
+                )
+            )
+            np.save(
+                os.path.join(tflite_model_name_dir, "hash"),
+                np.array(mlir_hash),
+            )
+
+
+# Validates whether the file is present or not.
+def is_valid_file(arg):
+    if not os.path.exists(arg):
+        return None
+    else:
+        return arg
+
+
+if __name__ == "__main__":
+    # 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)
+
+    # old_args = parser.parse_args()
+
+    home = str(Path.home())
+    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"
+    )
+
+    save_torch_model(
+        os.path.join(os.path.dirname(__file__), "tank", "torch_sd_list.csv")
+    )
+    save_torch_model(torch_model_csv)
+    save_tf_model(tf_model_csv)
+    save_tflite_model(tflite_model_csv)

inference/CMakeLists.txt

@@ -0,0 +1,192 @@
+# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+cmake_minimum_required(VERSION 3.17)
+
+project(sharkbackend LANGUAGES C CXX)
+
+#
+# Options
+#
+
+option(TRITON_ENABLE_GPU "Enable GPU support in backend" ON)
+option(TRITON_ENABLE_STATS "Include statistics collections in backend" ON)
+
+set(TRITON_COMMON_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/common repo")
+set(TRITON_CORE_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/core repo")
+set(TRITON_BACKEND_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/backend repo")
+
+if(NOT CMAKE_BUILD_TYPE)
+  set(CMAKE_BUILD_TYPE Release)
+endif()
+
+#
+# Dependencies
+#
+# FetchContent requires us to include the transitive closure of all
+# repos that we depend on so that we can override the tags.
+#
+include(FetchContent)
+
+FetchContent_Declare(
+  repo-common
+  GIT_REPOSITORY https://github.com/triton-inference-server/common.git
+  GIT_TAG ${TRITON_COMMON_REPO_TAG}
+  GIT_SHALLOW ON
+)
+FetchContent_Declare(
+  repo-core
+  GIT_REPOSITORY https://github.com/triton-inference-server/core.git
+  GIT_TAG ${TRITON_CORE_REPO_TAG}
+  GIT_SHALLOW ON
+)
+FetchContent_Declare(
+  repo-backend
+  GIT_REPOSITORY https://github.com/triton-inference-server/backend.git
+  GIT_TAG ${TRITON_BACKEND_REPO_TAG}
+  GIT_SHALLOW ON
+)
+FetchContent_MakeAvailable(repo-common repo-core repo-backend)
+
+#
+# The backend must be built into a shared library. Use an ldscript to
+# hide all symbols except for the TRITONBACKEND API.
+#
+configure_file(src/libtriton_dshark.ldscript libtriton_dshark.ldscript COPYONLY)
+
+add_library(
+  triton-dshark-backend SHARED
+  src/dshark.cc
+  #src/dshark_driver_module.c
+)
+
+add_library(
+  SharkBackend::triton-dshark-backend ALIAS triton-dshark-backend
+)
+
+target_include_directories(
+  triton-dshark-backend
+  PRIVATE
+    ${CMAKE_CURRENT_SOURCE_DIR}/src
+)
+
+list(APPEND CMAKE_MODULE_PATH "${PROJECT_BINARY_DIR}/lib/cmake/mlir")
+
+add_subdirectory(thirdparty/shark-runtime EXCLUDE_FROM_ALL)
+
+target_link_libraries(triton-dshark-backend PRIVATE iree_base_base
+  iree_hal_hal
+  iree_hal_cuda_cuda
+  iree_hal_cuda_registration_registration
+  iree_hal_vmvx_registration_registration
+  iree_hal_dylib_registration_registration
+  iree_modules_hal_hal
+  iree_vm_vm
+  iree_vm_bytecode_module
+  iree_hal_local_loaders_system_library_loader
+  iree_hal_local_loaders_vmvx_module_loader
+  )
+
+target_compile_features(triton-dshark-backend PRIVATE cxx_std_11)
+
+
+target_link_libraries(
+  triton-dshark-backend
+  PRIVATE
+    triton-core-serverapi   # from repo-core
+    triton-core-backendapi  # from repo-core
+    triton-core-serverstub  # from repo-core
+    triton-backend-utils    # from repo-backend
+)
+
+if(WIN32)
+  set_target_properties(
+    triton-dshark-backend PROPERTIES
+    POSITION_INDEPENDENT_CODE ON
+    OUTPUT_NAME triton_dshark
+  )
+else()
+  set_target_properties(
+    triton-dshark-backend PROPERTIES
+    POSITION_INDEPENDENT_CODE ON
+    OUTPUT_NAME triton_dshark
+    LINK_DEPENDS ${CMAKE_CURRENT_BINARY_DIR}/libtriton_dshark.ldscript
+    LINK_FLAGS "-Wl,--version-script libtriton_dshark.ldscript"
+  )
+endif()
+
+
+
+#
+# Install
+#
+include(GNUInstallDirs)
+set(INSTALL_CONFIGDIR ${CMAKE_INSTALL_LIBDIR}/cmake/SharkBackend)
+
+install(
+  TARGETS
+    triton-dshark-backend
+  EXPORT
+    triton-dshark-backend-targets
+  LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}/backends/dshark
+  RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/backends/dshark
+)
+
+install(
+  EXPORT
+    triton-dshark-backend-targets
+  FILE
+    SharkBackendTargets.cmake
+  NAMESPACE
+    SharkBackend::
+  DESTINATION
+    ${INSTALL_CONFIGDIR}
+)
+
+include(CMakePackageConfigHelpers)
+configure_package_config_file(
+  ${CMAKE_CURRENT_LIST_DIR}/cmake/SharkBackendConfig.cmake.in
+  ${CMAKE_CURRENT_BINARY_DIR}/SharkBackendConfig.cmake
+  INSTALL_DESTINATION ${INSTALL_CONFIGDIR}
+)
+
+install(
+  FILES
+  ${CMAKE_CURRENT_BINARY_DIR}/SharkBackendConfig.cmake
+  DESTINATION ${INSTALL_CONFIGDIR}
+)
+
+#
+# Export from build tree
+#
+export(
+  EXPORT triton-dshark-backend-targets
+  FILE ${CMAKE_CURRENT_BINARY_DIR}/SharkBackendTargets.cmake
+  NAMESPACE SharkBackend::
+)
+
+export(PACKAGE SharkBackend)
+

inference/README.md

@@ -0,0 +1,100 @@
+# SHARK Triton Backend
+
+The triton backend for shark.
+
+# Build
+
+Install SHARK
+
+```
+git clone https://github.com/nod-ai/SHARK.git
+# skip above step if dshark is already installed
+cd SHARK/inference
+```
+
+install dependancies
+
+```
+apt-get install patchelf rapidjson-dev python3-dev
+git submodule update --init
+```
+
+update the submodules of iree
+
+```
+cd thirdparty/shark-runtime
+git submodule update --init
+```
+
+Next, make the backend and install it
+
+```
+cd ../..
+mkdir build && cd build
+cmake -DTRITON_ENABLE_GPU=ON \
+-DIREE_HAL_DRIVER_CUDA=ON \
+-DIREE_TARGET_BACKEND_CUDA=ON \
+-DMLIR_ENABLE_CUDA_RUNNER=ON \
+-DCMAKE_INSTALL_PREFIX:PATH=`pwd`/install \
+-DTRITON_BACKEND_REPO_TAG=r22.02 \
+-DTRITON_CORE_REPO_TAG=r22.02 \
+-DTRITON_COMMON_REPO_TAG=r22.02 ..
+make install
+```
+
+# Incorporating into Triton
+
+There are much more in depth explenations for the following steps in triton's documentation:
+https://github.com/triton-inference-server/server/blob/main/docs/compose.md#triton-with-unsupported-and-custom-backends
+
+There should be a file at /build/install/backends/dshark/libtriton_dshark.so.  You will need to copy it into your triton server image.  
+More documentation is in the link above, but to create the docker image, you need to run the compose.py command in the triton-backend server repo
+
+
+To first build your image, clone the tritonserver repo.
+
+```
+git clone https://github.com/triton-inference-server/server.git
+```
+
+then run `compose.py` to build a docker compose file 
+```
+cd server
+python3 compose.py --repoagent checksum --dry-run
+```
+
+Because dshark is a third party backend, you will need to manually modify the `Dockerfile.compose` to include the dshark backend.  To do this, in the Dockerfile.compose file produced, copy this line.
+the dshark backend will be located in the build folder from earlier under `/build/install/backends`
+
+```
+COPY /path/to/build/install/backends/dshark /opt/tritonserver/backends/dshark
+```
+
+Next run 
+```
+docker build -t tritonserver_custom -f Dockerfile.compose .
+docker run -it --gpus=1 --net=host -v/path/to/model_repos:/models  tritonserver_custom:latest tritonserver --model-repository=/models
+```
+
+where `path/to/model_repos` is where you are storing the models you want to run
+
+if your not using gpus, omit `--gpus=1`
+
+```
+docker run -it  --net=host -v/path/to/model_repos:/models  tritonserver_custom:latest tritonserver --model-repository=/models
+```
+
+# Setting up a model
+
+to include a model in your backend, add a directory with your model name to your model repository directory.  examples of models can be seen here: https://github.com/triton-inference-server/backend/tree/main/examples/model_repos/minimal_models
+
+make sure to adjust the input correctly in the config.pbtxt file, and save a vmfb file under 1/model.vmfb
+
+# CUDA
+
+if you're having issues with cuda, make sure your correct drivers are installed, and that `nvidia-smi` works, and also make sure that the nvcc compiler is on the path.
+
+
+
+
+

inference/cmake/SharkBackendConfig.cmake.in

@@ -0,0 +1,39 @@
+# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+include(CMakeFindDependencyMacro)
+
+get_filename_component(
+  SHARKBACKEND_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH
+)
+
+list(APPEND CMAKE_MODULE_PATH ${SHARKBACKEND_CMAKE_DIR})
+
+if(NOT TARGET SharkBackend::triton-dshark-backend)
+  include("${SHARKBACKEND_CMAKE_DIR}/SharkBackendTargets.cmake")
+endif()
+
+set(SHARKBACKEND_LIBRARIES SharkBackend::triton-dshark-backend)

inference/src/libtriton_dshark.ldscript

@@ -0,0 +1,30 @@
+# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+{
+  global:
+    TRITONBACKEND_*;
+  local: *;
+};

process_skipfiles.py

@@ -6,15 +6,36 @@ from distutils.sysconfig import get_python_lib
 import fileinput
 from pathlib import Path
 
-# Temporary workaround for transformers/__init__.py.
-path_to_transformers_hook = Path(
-    get_python_lib() + "/_pyinstaller_hooks_contrib/hooks/stdhooks/hook-transformers.py"
+# Diffusers 0.13.1 fails with transformers __init.py errros in BLIP. So remove it for now until we fork it
+pix2pix_init = Path(get_python_lib() + "/diffusers/__init__.py")
+for line in fileinput.input(pix2pix_init, inplace=True):
+    if "Pix2Pix" in line:
+        if not line.startswith("#"):
+            print(f"#{line}", end="")
+        else:
+            print(f"{line[1:]}", end="")
+    else:
+        print(line, end="")
+pix2pix_init = Path(get_python_lib() + "/diffusers/pipelines/__init__.py")
+for line in fileinput.input(pix2pix_init, inplace=True):
+    if "Pix2Pix" in line:
+        if not line.startswith("#"):
+            print(f"#{line}", end="")
+        else:
+            print(f"{line[1:]}", end="")
+    else:
+        print(line, end="")
+pix2pix_init = Path(
+    get_python_lib() + "/diffusers/pipelines/stable_diffusion/__init__.py"
 )
-if path_to_transformers_hook.is_file():
-    pass
-else:
-    with open(path_to_transformers_hook, "w") as f:
-        f.write("module_collection_mode = 'pyz+py'")
+for line in fileinput.input(pix2pix_init, inplace=True):
+    if "StableDiffusionPix2PixZeroPipeline" in line:
+        if not line.startswith("#"):
+            print(f"#{line}", end="")
+        else:
+            print(f"{line[1:]}", end="")
+    else:
+        print(line, end="")
 
 path_to_skipfiles = Path(get_python_lib() + "/torch/_dynamo/skipfiles.py")
 
@@ -55,12 +76,3 @@ for line in fileinput.input(path_to_lazy_loader, inplace=True):
         )
     else:
         print(line, end="")
-
-# For getting around timm's packaging.
-# Refer: https://github.com/pyinstaller/pyinstaller/issues/5673#issuecomment-808731505
-path_to_timm_activations = Path(get_python_lib() + "/timm/layers/activations_jit.py")
-for line in fileinput.input(path_to_timm_activations, inplace=True):
-    if "@torch.jit.script" in line:
-        print("@torch.jit._script_if_tracing", end="\n")
-    else:
-        print(line, end="")

pyproject.toml

@@ -5,25 +5,13 @@ requires = [
     "packaging",
 
     "numpy>=1.22.4",
+    "torch-mlir>=20221021.633",
     "iree-compiler>=20221022.190",
     "iree-runtime>=20221022.190",
 ]
 build-backend = "setuptools.build_meta"
 
 [tool.black]
+line-length = 79
 include = '\.pyi?$'
-exclude = '''
-(
-  /(
-    | apps/stable_diffusion
-    | apps/language_models
-    | shark
-    | benchmarks
-    | tank
-    | build
-    | generated_imgs
-    | shark.venv
-  )/
-  | setup.py
-)
-'''
+

pytest.ini

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

requirements-importer-macos.txt

@@ -8,8 +8,19 @@ torchvision
 tqdm
 
 #iree-compiler  | iree-runtime should already be installed
+#these dont work ok osx
+#iree-tools-tflite
+#iree-tools-xla
+#iree-tools-tf
 
+# TensorFlow and JAX.
+gin-config
+tensorflow-macos
+tensorflow-metal
+#tf-models-nightly
+#tensorflow-text-nightly
 transformers
+tensorflow-probability
 #jax[cpu]
 
 # tflitehub dependencies.

requirements-importer.txt

@@ -2,20 +2,30 @@
 --pre
 
 numpy>1.22.4
+torchvision
 pytorch-triton
-torchvision 
 tabulate
 
 tqdm
 
 #iree-compiler  | iree-runtime should already be installed
+iree-tools-tflite
 iree-tools-xla
+iree-tools-tf
 
-# Modelling and JAX.
+# TensorFlow and JAX.
 gin-config
+tf-nightly
+keras>=2.10
+#tf-models-nightly
+#tensorflow-text-nightly
 transformers
 diffusers
+#tensorflow-probability
 #jax[cpu]
+
+
+# tflitehub dependencies.
 Pillow
 
 # Testing and support.
@@ -23,10 +33,9 @@ lit
 pyyaml
 python-dateutil
 sacremoses
-sentencepiece
 
 # web dependecies.
-gradio==3.44.3
+gradio
 altair
 scipy