The closest documented route is stable-diffusion.cpp: its project documentation lists Vulkan, Android support through Termux or Local Diffusion, and quantized GGUF model weights. That combination makes it a practical starting point—not a guarantee that a particular phone, GPU driver, model, or quantization will work. Build for the Android target, confirm Vulkan is the backend actually in use, and test on the device you intend to use.
Which Android Vulkan runtime should you start with?
Start with stable-diffusion.cpp. The project documents CPU, CUDA, Vulkan, Metal, OpenCL and SYCL backends, Android use through Termux or Local Diffusion, and support for formats including GGUF. Check its current README and build documentation before choosing a model or building: support can change, and a desktop Vulkan build is not the same thing as an Android package.
This is a project-level match among diffusion inference, Android and Vulkan. It is not a verified compatibility list for Android phones. The available project documentation does not establish which specific phone, GPU and driver combinations run every supported model and quantization successfully.
Choose a model and quantization
The project documents f32 and f16 weights as well as the quantized types q8_0, q5_0, q5_1, q4_0 and q4_1. It supports GGUF, and its documentation describes converting supported source weights to GGUF ahead of loading. That avoids repeating conversion at every load, but does not make an unsupported architecture or checkpoint compatible.
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Check the model’s architecture and license separately from its file format. A model being available as a checkpoint or convertible to GGUF does not by itself establish that the project supports its architecture or that its license permits your intended use.
Published memory estimates for Stable Diffusion 1.x
The following are estimates in the stable-diffusion.cpp documentation for 512×512 text-to-image generation. They are not independent tests, Android-specific measurements or guarantees of peak memory on a Vulkan phone.
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| Weight type | Without Flash Attention | With Flash Attention |
|---|---|---|
| f32 | Approximately 2.8 GB | Approximately 2.4 GB |
| f16 | Approximately 2.3 GB | Approximately 1.9 GB |
| q8_0 | Approximately 2.1 GB | Approximately 1.6 GB |
| q5 and q4 variants | Approximately 2.0 GB | Approximately 1.5 GB |
These figures are useful for comparing the project’s documented configurations, not for deciding that a phone with a similar amount of memory will necessarily complete a generation. Device memory available to the process, model details, build options and runtime behavior also matter.
Build and run it on the Android target
- Confirm the target and backend. In the current
stable-diffusion.cppREADME and build documentation, verify that Android and Vulkan are still documented for your intended setup. Choose the Termux or Local Diffusion route appropriate to how you want to run it. - Set up the Android build. Follow the project’s Android NDK instructions for the target. Use its Vulkan build guidance as well; do not assume that a desktop build command creates an Android app or that a build configured for another backend is using Vulkan.
- Prepare a compatible model. Select a checkpoint whose architecture the project supports, check its license, and choose a documented weight type such as q8_0, q5_0/q5_1 or q4_0/q4_1. Convert supported source weights to GGUF in advance if that is the format you plan to load.
- Check backend selection at runtime. Run the project using the configuration documented for your Android build and verify that Vulkan—not CPU, OpenCL or another available backend—is selected. A successful build alone does not prove that inference is using Vulkan.
- Start with a small generation. Use a modest test generation, then increase workload only after it completes reliably. Record the model, weight type, image dimensions and step count so later runs can be compared on the same basis.
- Measure the actual phone. For a useful performance report, note the phone and chipset, Android version, GPU driver, project revision, model, quantization, image dimensions, denoising steps, latency and peak memory. Report results only for the device and build measured.
The project documentation also describes Android OpenCL setup. OpenCL is a different backend: using those instructions does not establish a Vulkan build or Vulkan performance.
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How quantization affects the choice
Quantization reduces the precision used to represent model weights. The project’s memory table estimates lower memory use for its q8, q5 and q4 configurations than for f32 or f16 under the stated Stable Diffusion 1.x, 512×512 conditions. The estimates do not establish that every quantized type is supported by every model or performs best on a given Android GPU.
Choose a type based on compatibility and measured behavior on the target device, not just the smallest published memory estimate. No Android Vulkan quality comparisons or speed tests for these weight types are provided in the cited documentation, so it does not support claiming that one is universally the best balance.
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How Vulkan compares with other Android diffusion routes
Performance numbers from different execution paths are not directly comparable unless the device, model, image size, step count and measurement method match. In particular, Android GPU execution through Vulkan is distinct from a vendor NPU runtime or TensorFlow Lite GPU inference.
| Route | What the cited material establishes | What it does not establish |
|---|---|---|
stable-diffusion.cpp with Vulkan |
Project documentation lists Vulkan, Android via Termux or Local Diffusion, and quantized/GGUF support. | A verified list of compatible Android Vulkan phone/GPU/driver combinations or universal performance. |
| Qualcomm AI Engine | Qualcomm demonstrated quantized Stable Diffusion inference on a Snapdragon 8 Gen 2 phone using its AI Engine hardware acceleration. | Vulkan performance. The demonstration used a Qualcomm AI Engine path, not Vulkan. |
| TensorFlow Lite Mobile Stable Diffusion | A 2023 paper by Choi et al. from SqueezeBits and Seoul National University reported approximately 7 seconds for a 512×512 image on a Samsung Galaxy S23 using Mobile Stable Diffusion based on Stable Diffusion 2.1. | A Vulkan result. The implementation used TensorFlow Lite, not Vulkan. |
| ExecuTorch Vulkan | The cited v1.0.1-rc1 overview describes a Vulkan backend focused on Android GPUs. | Mature, complete quantized diffusion operator coverage. That versioned overview says additional quantized operators and modes were still in development. |
Qualcomm’s separate quantization tutorial describes quantizing the text encoder, UNet and VAE components individually for Stable Diffusion 2.1. It uses 20 diffusion steps on 100 prompts by default for calibration, notes CPU quantization may take hours, evaluates quantization in simulation, and then compiles with AI Hub Workbench. The tutorial says it does not currently provide an Android sample app; it is not a turnkey Vulkan workflow.
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Qualcomm reported under 15 seconds for a 512×512 image at 20 inference steps in its 2023 Snapdragon 8 Gen 2 / Qualcomm AI Engine demonstration. That number describes that specific vendor-accelerated demonstration, not a Vulkan result. Qualcomm’s later AI Hub model materials cover Qualcomm runtimes and compilations, which should not be treated as interchangeable with a Vulkan build.
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