Agile Diffusers Inference (ADI)

Agile Diffusers Inference (ADI) is a C++ library with CLI tool. Purpose to leverage the acceleration capabilities of ONNXRuntime and the high compatibility of the .onnx model format to provide a convenient solution for the engineering deployment of Stable Diffusion, with suitable package size & high performance.

Why choose ONNXRuntime as our Inference Engine?

Open Source: ONNXRuntime is an open-source project, allowing users to freely use and modify it to suit different application scenarios.
Scalability: It supports custom operators and optimizations, allowing for extensions and optimizations based on specific needs.
High Performance: ONNXRuntime is highly optimized to provide fast inference speeds, suitable for real-time applications.
Strong Compatibility: It supports model conversion from multiple deep learning frameworks (such as PyTorch, TensorFlow), making integration and deployment convenient.
Cross-Platform Support: ONNXRuntime supports multiple hardware platforms, including CPU, GPU, TPU, etc., enabling efficient execution on various devices.
Community and Enterprise Support: Developed and maintained by Microsoft, it has an active community and enterprise support, providing continuous updates and maintenance.

How to install (CLI)?

Method 1: Install the Command Line Tool Using a Package Manager

## macOS (Homebrew):
brew tap windsander/adi-stable-diffusion
brew install adi

## Windows (git-Bash + Chocolatey):
curl -L -o adi.1.0.1.nupkg "https://raw.githubusercontent.com/Windsander/ADI-Stable-Diffusion/deploy/adi.1.0.1.nupkg"
choco install adi.1.0.1.nupkg -y

Method 2: Download from the Released Version

You can find the latest available version from the Release Assets. The file tree of the package will look like this:

--bin
    --adi
--lib
    --[Corresponding platform's ADI library, e.g., libadi.a]
    --[Corresponding platform's ORT library, e.g., libonnxruntime.dylib]
--include
    --adi.h
--CHANGELOG.md
--README.md
--LICENSE

After unzipping, you can simply install the bin and lib directories to your system, or just go into the unzipped bin directory, and start using adi.

Method 3: Build [adi-lib & adi-cli] Locally

An automated script is provided to compile ADI on your device more easily.

Simply execute the script auto_build.sh:

# if you do not pass the BUILD_TYPE parameter, the script will use the default Debug build type.
# and, if you not enable certain ORTProvider by [options]], script will choose default ORTProvider by platform
bash ./auto_build.sh

# Example-MacOS:
bash ./auto_build.sh --platform macos --build-type debug
           
# Example-Windows:
bash ./auto_build.sh --platform windows --build-type debug
                    
# Example-Linux(Ubuntu):
bash ./auto_build.sh --platform linux --build-type debug
           
# Example-Android:
bash ./auto_build.sh --platform android \
           --build-type debug \
           --android-ndk /Volumes/AL-Data-W04/WorkingEnv/Android/sdk/ndk/26.1.10909125 \
           --android-ver 27
           
# Example(with Extra Options) as below, build release with CUDA=ON TensorRT=ON, and custom compiler configs
bash ./auto_build.sh [params] \
           --cmake /opt/homebrew/Cellar/cmake/3.29.5/bin/cmake \
           --ninja /usr/local/bin/ninja \
           --arch-abi x86_64 \
           --jobs 8 \
           --options "-DORT_ENABLE_CUDA=ON -DORT_ENABLE_TENSOR_RT=ON"

currently, this project provide below [Options]:

# 1. Option list
option(ORT_COMPILED_ONLINE           "adi: using online onnxruntime(ort), otherwise local build" ${SD_ORT_ONLINE_AVAIL})
option(ORT_COMPILED_HEAVY            "adi: using HEAVY compile, ${Red}only for debug, default OFF${ColourReset}" OFF)
option(ORT_BUILD_COMMAND_LINE        "adi: build command line tools" ${CMAKE_STANDALONE})
option(ORT_BUILD_COMBINE_BASE        "adi: build combine code together to build a single output lib" OFF)
option(ORT_BUILD_SHARED_ADI          "adi: build ADI project shared libs" OFF)
option(ORT_BUILD_SHARED_ORT          "adi: build ORT in shared libs" OFF)
option(ORT_ENABLE_TENSOR_RT          "adi: using TensorRT provider to accelerate inference" ${DEFAULT_TRT_STATE})
option(ORT_ENABLE_CUDA               "adi: using CUDA provider to accelerate inference" ${DEFAULT_CUDA_STATE})
option(ORT_ENABLE_COREML             "adi: using CoreML provider to accelerate inference" ${DEFAULT_COREML_STATE})
option(ORT_ENABLE_NNAPI              "adi: using NNAPI provider to accelerate inference" ${DEFAULT_NNAPI_STATE})
option(ADI_AUTO_INSTALL              "adi: auto-install ADI-CLI to current system when build finish, request admin permission" OFF)

enable if you have to (ONLY FOR YOU TRULY NEEDS, UNRECOMMENDED).

How to use?

Example: 1-step Euler_A img2img latent space visualized

Below show What actually happened in [Example: 1-step img2img inference] in Latent Space (Skip All Models):
You can use the command-line tools generated by CMake to execute the relevant functionalities of this project

doing 1-step img2img inference, like:

# Optional(if using local build & not install): cd to ./[your_adi_path]/bin/ ,like: 
cd ./cmake-build-debug/bin/

# and here is an example of using this tool:
# sd-turbo, img2img, positive, inference_steps=1, guide=1.0, euler_a(for 1-step purpose)
adi \
 -p "A cat in the water at sunset" \
 -m img2img \
 -i ../../sd/io-test/input-test.png \
 -o ../../sd/io-test/output.png \
 -w 512 -h 512 -c 3 \
 --seed 15.0 \
 --dims 1024 \
 --clip ../../sd/sd-base-model/onnx-sd-turbo/text_encoder/model.onnx \
 --unet ../../sd/sd-base-model/onnx-sd-turbo/unet/model.onnx \
 --vae-encoder ../../sd/sd-base-model/onnx-sd-turbo/vae_encoder/model.onnx \
 --vae-decoder ../../sd/sd-base-model/onnx-sd-turbo/vae_decoder/model.onnx \
 --dict ../../sd/sd-dictionary/vocab.txt \
 --beta-start 0.00085 \
 --beta-end 0.012 \
 --beta scaled_linear \
 --alpha cos \
 --scheduler euler_a \
 --predictor epsilon \
 --tokenizer bpe \
 --train-steps 1000 \
 --token-idx-num 49408 \
 --token-length 77 \
 --token-border 1.0 \
 --gain 1.1 \
 --decoding 0.18215 \
 --guidance 1.0 \
 --steps 1 \
 -v

And now, you can have a try~ (0w0 )

Extra intelligence：

Manually Prepare Inference Engine, see at: Engine's README.md
Manually Prepare ONNX-Format Converter & SD-Models, see at: SD_ORT's README.md

Development Progress Checklist (latest):

Basic Pipeline Functionalities (Major)

[SD_v1] Stable-Diffusion (v1.0 ~ v1.5, turbo) (after 2024/06/04 tested)
- v1.0 (HuggingFace): Initial version ✅
- v1.1 (HuggingFace): Improved image quality and generation speed ✅
- v1.2 (HuggingFace): Further optimized generation effects ✅
- v1.3 (HuggingFace): Added more training data ✅
- v1.4 (HuggingFace): Enhanced image generation diversity ✅
- v1.5 (HuggingFace): Final optimized version ✅
- turbo (HuggingFace): Community-driven optimized version, faster and efficiency ✅
[SD_v2] Stable-Diffusion (v2.0, v2.1)
- v2.0 (HuggingFace): Significant improvements in image quality and generation efficiency
- v2.1 (HuggingFace): Further optimized model stability and generation effects
[SD_v3] Stable-Diffusion (v3.0)
- v3.0 (HuggingFace): Anticipated next-generation version with more improvements and new features
[SDXL] Stable-Diffusion-XL
- SDXL (HuggingFace): Experimental version for larger-scale models and higher-resolution image
- SDXL-turbo (HuggingFace): Community-driven optimized version, faster and efficiency
[SVD] Stable-Video-Diffusion
- SVD (HuggingFace): Version specifically for video generation and editing

Scheduler Abilities

Tokenizer Type

Byte-Pair Encoding (bpe) (after 2024/07/03 ✅tested)
Word Piece Encoding (wp) (after 2024/05/27 ✅tested)
Sentence Piece Encoding (sp) [if necessary]