For an Android diffusion workflow that uses Vulkan, start with stable-diffusion.cpp. Its project documentation lists Android support, Vulkan as a backend, and quantized GGUF weights in the same project. That makes it the closest documented fit for this task—not a guarantee that every phone, GPU driver, model architecture, or quantization type 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 use?
Start with stable-diffusion.cpp
The stable-diffusion.cpp documentation lists Vulkan among its backends and Android via Termux or Local Diffusion among its supported platforms. It also documents GGUF and quantized model weights. Because those requirements appear together in one project, this is the most direct starting point for Android diffusion inference on Vulkan.
Check the project’s current README and build documentation before choosing a model or compiling. Confirm that the current revision supports your target Android setup, the Vulkan backend, and the model architecture you plan to run. Project support is not the same as a verified compatibility list: the reviewed documentation does not establish which phone, GPU, and driver combinations successfully run every model.
Keep other Android acceleration paths separate
A phone running a diffusion model does not necessarily mean it is using Vulkan. Qualcomm’s Stable Diffusion demonstration used Qualcomm AI Engine hardware acceleration on a Snapdragon 8 Gen 2, not Vulkan. Mobile Stable Diffusion research reporting a TensorFlow Lite GPU implementation is also a separate path. Those examples show that mobile diffusion inference is possible, but they do not verify a Vulkan build.
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ExecuTorch has an Android-focused Vulkan backend, but its v1.0.1-rc1 overview says additional quantized operators and modes were still being added. That documentation is not evidence of a mature, turnkey quantized diffusion workflow with complete operator coverage.
Choose and prepare a quantized model
Check architecture and license first
Pick a checkpoint supported by the current project revision, then check that checkpoint’s license and usage terms separately. Support for a weight format does not establish support for every model architecture, and neither establishes permission to use a particular checkpoint for your intended purpose.
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Choose a documented weight type
The project’s quantization documentation lists q8_0, q5_0, q5_1, q4_0, and q4_1, as well as f16 and f32. It describes converting supported source weights to GGUF in advance. Preparing GGUF ahead of loading can avoid repeating conversion whenever the model is loaded; confirm the conversion procedure and compatibility requirements in the project’s current documentation.
Lower-bit weights can reduce the model’s memory footprint, but the figures below are project documentation estimates for Stable Diffusion 1.x text-to-image at 512 × 512. They are not independent measurements or Android Vulkan guarantees.
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| Weight type | Estimated memory without Flash Attention | Estimated memory with Flash Attention |
|---|---|---|
f32 |
Approximately 2.8 GB (stable-diffusion.cpp project estimate, accessed 2026) | Approximately 2.4 GB (stable-diffusion.cpp project estimate, accessed 2026) |
f16 |
Approximately 2.3 GB (stable-diffusion.cpp project estimate, accessed 2026) | Approximately 1.9 GB (stable-diffusion.cpp project estimate, accessed 2026) |
q8_0 |
Approximately 2.1 GB (stable-diffusion.cpp project estimate, accessed 2026) | Approximately 1.6 GB (stable-diffusion.cpp project estimate, accessed 2026) |
q5 and q4 variants |
Approximately 2.0 GB (stable-diffusion.cpp project estimate, accessed 2026) | Approximately 1.5 GB (stable-diffusion.cpp project estimate, accessed 2026) |
These figures describe the project’s estimate for the stated model family and image size, not total phone memory requirements. Actual use can depend on the model, build, backend, and generation settings; leave room for Android and other running processes rather than treating an estimate as a device minimum.
Build and run it on the Android device
- Verify the target and backend. Read the current
stable-diffusion.cppREADME and build instructions. Confirm that Android and Vulkan are supported by the revision you intend to build. The project also documents Android OpenCL setup; OpenCL is a different backend and should not be mistaken for Vulkan. - Follow Android-specific build instructions. Use the project’s Android NDK/build guidance for the intended target, then follow its Vulkan-specific instructions. A desktop Vulkan build command by itself does not produce an Android package or prove that an Android Vulkan backend was built.
- Prepare a compatible model. Confirm the checkpoint architecture and supported source format, convert to a documented GGUF quantization type if needed, and place the resulting model where the Android runtime can access it.
- Confirm which backend is executing. Use the project’s documented runtime or build options to verify Vulkan selection. Do not infer Vulkan use merely because the program runs on an Android phone or uses a GPU-capable build.
- Run a small test generation. Start with modest image dimensions and a low step count, then increase them only if the generation completes reliably and memory use is acceptable. The reviewed documentation does not provide a universal command line or Android UI path that can be assumed across project revisions and packaging choices.
- Record the test conditions. For a reproducible result, note the phone and chipset, Android version, GPU driver, project revision, model and quantization, image dimensions, step count, latency, and peak memory. Treat performance as specific to that setup.
What results can you expect?
There is no source-backed universal speed claim for quantized diffusion on Android Vulkan. The available headline figures use different devices and execution backends, so they cannot predict Vulkan performance.
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| Published result | Device and method | What it does—and does not—show |
|---|---|---|
| Under 15 seconds for a 512 × 512 image at 20 inference steps (Qualcomm, 2023) | Snapdragon 8 Gen 2 using Qualcomm AI Engine hardware acceleration | A Qualcomm acceleration demonstration; not a Vulkan benchmark. |
| Approximately 7 seconds for a 512 × 512 image (Choi et al., SqueezeBits and Seoul National University, 2023) | Samsung Galaxy S23; Mobile Stable Diffusion based on Stable Diffusion 2.1 using TensorFlow Lite | A TensorFlow Lite mobile result; not a Vulkan benchmark. |
Resolution, denoising steps, model, device, and runtime all affect latency. Compare results only when those conditions are sufficiently alike; do not use either result as a promise for an Android Vulkan setup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should you consider a different route?
Qualcomm AI Engine and AI Hub
Qualcomm’s Android demonstration is relevant if you are investigating its vendor-specific AI Engine route, but it does not validate Vulkan. Qualcomm’s Stable Diffusion 2.1 quantization tutorial describes quantizing the text encoder, UNet, and VAE separately. Its default calibration uses 20 diffusion steps across 100 prompts; the tutorial notes CPU quantization may take hours, evaluates quantization in simulation, and then compiles with AI Hub Workbench. It explicitly says an Android sample app is not currently provided for that workflow.
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Qualcomm AI Hub Models lists Android runtimes including Qualcomm AI Engine Direct, LiteRT, and ONNX, with CPU, GPU, and NPU precision support varying by unit. The Stable Diffusion 1.5 mobile catalog page reviewed for this article displayed “This model is currently not supported on any Mobile chipset,” despite listing a broader device and chipset catalog. That is a catalog status observed at the time of review, not a general statement about all Qualcomm runtimes or devices; check the current model page before relying on it.
TensorFlow Lite Mobile Stable Diffusion
The Mobile Stable Diffusion work is useful evidence of an Android GPU implementation, but it uses TensorFlow Lite rather than the Vulkan route described here. Its published result should be treated as evidence for that implementation only.
ExecuTorch Vulkan
ExecuTorch’s Vulkan backend targets Android GPUs, but the cited v1.0.1-rc1 overview says quantized operator and mode support was still expanding. Check the current operator coverage against the chosen diffusion model before treating it as a viable quantized alternative.
Quick Recap
What to verify before relying on a setup
- The project revision supports Android, Vulkan, and the model architecture you selected.
- The chosen quantization type and model conversion path are supported by that revision.
- The phone’s Android software and Vulkan driver work with the build; the reviewed documentation does not provide a verified phone/GPU/driver compatibility matrix for this workflow.
- The runtime actually selects Vulkan rather than CPU, OpenCL, or a vendor-specific accelerator.
- A test generation completes at the intended dimensions and step count without unacceptable memory pressure.
- Any speed or memory figure you publish is measured on the named device and build, with the model and generation settings stated.
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