Local Dream

Android Stable Diffusion with Snapdragon NPU acceleration
Also supports CPU/GPU inference

About this Repo

This project is now open sourced and completely free. Hope you enjoy it!

If you like it, please consider sponsor this project.

🚀 Quick Start

1. Download: Get the APK from Releases or Google Play(NSFW filtered)
2. Install: Install the APK on your Android device
3. Select Models: Open the app and download the model(s) you want to use

✨ Features

• 🎨 txt2img - Generate images from text descriptions
• 🖼️ img2img - Transform existing images
• 🎭 inpaint - Redraw selected areas of images

🔧 Build Instructions

Note: Building on Linux/WSL is recommended. Other platforms are not verified.

Prerequisites

The following tools are required for building:

• Rust - Install rustup, then run:

  rustup default stable
  rustup target add aarch64-linux-android

• Ninja - Build system
• CMake - Build configuration

1. Clone Repository

git clone --recursive https://github.com/xororz/local-dream.git

2. Prepare SDKs

1. Download QNN SDK: Get QNN_SDK_2.29 and extract
2. Download Android NDK: Get Android NDK and extract
3. Configure paths:
   • Update QNN_SDK_ROOT in app/src/main/cpp/CMakeLists.txt
   • Update ANDROID_NDK_ROOT in app/src/main/cpp/CMakePresets.json

3. Build Libraries

🐧 Linux

cd app/src/main/cpp/
bash ./build.sh

🪟 Windows

# Install dependencies if needed:
# winget install Kitware.CMake
# winget install Ninja-build.Ninja
# winget install Rustlang.Rustup

cd app\src\main\cpp\

# Convert patch file (install dos2unix if needed: winget install -e --id waterlan.dos2unix)
dos2unix SampleApp.patch
.\build.bat

🍎 macOS

# Install dependencies with Homebrew:
# brew install cmake rust ninja

# Fix CMake version compatibility
sed -i '' '2s/$/ -DCMAKE_POLICY_VERSION_MINIMUM=3.5/' build.sh
bash ./build.sh

4. Build APK

Open this project in Android Studio and navigate to: Build → Generate App Bundles or APKs → Generate APKs

Technical Implementation

NPU Acceleration

• SDK: Qualcomm QNN SDK leveraging Hexagon NPU
• Quantization: W8A16 static quantization for optimal performance
• Resolution: Fixed 512×512 model shape
• Performance: Extremely fast inference speed

CPU/GPU Inference

• Framework: Powered by MNN framework
• Quantization: W8 dynamic quantization
• Resolution: Flexible sizes (128×128, 256×256, 384×384, 512×512)
• Performance: Moderate speed with high compatibility

NPU High Resolution Support

After downloading a 512 resolution model, you can download patches to enable 768×768 and 1024×1024 image generation. Please note that quantized high-resolution models may produce images with poor layout. We recommend first generating at 512 resolution, then using the high-resolution model for img2img (which is essentially Highres.fix). The suggested img2img denoise_strength is around 0.75.

Device Compatibility

NPU Acceleration Support

Compatible with devices featuring:

• Snapdragon 8 Gen 1
• Snapdragon 8+ Gen 1
• Snapdragon 8 Gen 2
• Snapdragon 8 Gen 3
• Snapdragon 8 Elite

Note: Other devices cannot download NPU models

CPU/GPU Support

• RAM Requirement: ~2GB available memory
• Compatibility: Most Android devices from recent years

Available Models

Now supports importing from local SD1.5 based safetensor for CPU/GPU.

Model	Type	CPU/GPU	NPU	Clip Skip	Source
Anything V5.0	SD1.5	✅	✅	2	CivitAI
ChilloutMix	SD1.5	✅	✅	1	CivitAI
Absolute Reality	SD1.5	✅	✅	2	CivitAI
QteaMix	SD1.5	✅	✅	2	CivitAI
CuteYukiMix	SD1.5	✅	✅	2	CivitAI
Stable Diffusion 2.1	SD2.1	❌	✅	1	HuggingFace
Pony V5.5	SD2.1	❌	✅	1	CivitAI

🎲 Seed Settings

Custom seed support for reproducible image generation:

• CPU Mode: Seeds guarantee identical results across different devices with same parameters
• GPU Mode: Results may differ from CPU mode and can vary between different devices
• NPU Mode: Seeds ensure consistent results only on devices with identical chipsets

Credits & Acknowledgments

C++ Libraries

• Qualcomm QNN SDK - NPU model execution
• alibaba/MNN - CPU model execution
• xtensor-stack - Tensor operations & scheduling
• mlc-ai/tokenizers-cpp - Text tokenization
• yhirose/cpp-httplib - HTTP server
• nothings/stb - Image processing
• facebook/zstd - Model compression
• nlohmann/json - JSON processing

Android Libraries

• coil-kt/coil - Image loading & processing
• MoyuruAizawa/Cropify - Image cropping
• AOSP, Material Design, Jetpack Compose - UI framework

NSFW Detection Model

• bhky/opennsfw2 - NSFW content filtering

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💖 Support This Project

If you find Local Dream useful, please consider supporting its development:

What Your Support Helps With:

• Additional Models - More AI model integrations
• New Features - Enhanced functionality and capabilities
• Bug Fixes - Continuous improvement and maintenance

Your sponsorship helps maintain and improve Local Dream for everyone!