Not as a documented, ready-to-use stack. The available documentation points to two different routes: LiteRT describes Android GPU inference that can handle some quantized models through a floating-point GPU path, while ExecuTorch documents an Android-focused Vulkan backend with support for quantized linear layers. Neither establishes that a complete quantized diffusion model will run efficiently on Android Vulkan, and neither demonstrates real-time texture synthesis. Treat this as a model-compatibility and end-to-end performance project, not a wiring exercise with a guaranteed result.
What does “Android GPU inference” mean here?
A GPU delegate or backend is not automatically a Vulkan backend. LiteRT and ExecuTorch are separate runtimes, and their Android GPU paths should not be treated as interchangeable.
| Route | What the cited documentation establishes | What it does not establish |
|---|---|---|
| LiteRT GPU | LiteRT documents Android GPU inference, a finite set of supported operations, and a way to execute supported 8-bit quantized models as a floating-point view on the GPU. | The LiteRT Android GPU path is not established as Vulkan. The LiteRT project platform table lists Android GPU APIs as OpenCL and OpenGL. |
| ExecuTorch Vulkan | The official Vulkan overview describes a backend developed with a focus on Android GPUs, packaged through executorch-android-vulkan. It says quantized linear layers are supported. |
The overview does not establish support for every quantized operator or a complete quantized diffusion graph. Additional quantized operators and modes are described as in progress. |
These are alternative runtime/backend choices, not components to combine casually. If Vulkan is a hard requirement, investigate the model against ExecuTorch’s Vulkan backend and the exact release you intend to ship. If the priority is using LiteRT’s documented Android GPU route, do not describe that route as Vulkan.
Why is a quantized diffusion model a harder fit than a single GPU operation?
Quantization support depends on operations and backend
A quantized model is a graph of operations, not one indivisible object. LiteRT’s GPU guide describes handling supported 8-bit models by executing a floating-point view: constant tensors such as weights and biases are dequantized into GPU memory when the delegate is enabled. Quantized inputs and outputs may be converted on the CPU for every inference, and quantization simulators are inserted between operations to preserve learned activation bounds. The guide recommends floating-point model input and output tensors for performance.
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LiteRT also documents a finite supported-operation set. If the model includes unsupported operations, execution can be split between CPU and GPU. The guide warns that CPU/GPU synchronization can make split execution slower than CPU-only execution. For diffusion, a delegate accepting the model is not enough: determine which operations run on which processor and whether the resulting transfers erase the benefit of GPU acceleration.
A diffusion denoiser needs a graph-level audit
Before choosing a backend, inventory the exported model’s operators, tensor shapes, precision, and conversions, then compare every operation with the chosen runtime’s support for the target backend. Check the denoiser and any other model components in the generation path; do not assume that support for one operator proves support for the whole pipeline. The cited documentation does not provide a model-specific compatibility result for a diffusion denoiser.
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For an ExecuTorch Vulkan candidate, quantized linear-layer support is only one part of that audit. Verify the exact exported graph against the exact release and Vulkan partitioner behavior. The cited overview does not establish that arbitrary quantized diffusion operations or a complete end-to-end quantized diffusion export are supported.
How should you approach the integration?
- Define the output workload. Decide whether the application generates a single tile on demand, streams texture updates, or continuously evolves a texture. Those workloads need different latency and quality targets; the available sources do not benchmark them.
- Fix the Vulkan requirement. If the application must use Vulkan, evaluate the ExecuTorch Vulkan route rather than assuming LiteRT’s Android GPU delegate is Vulkan. If the requirement is simply GPU acceleration, assess LiteRT on its documented Android route as a separate option.
- Audit the exported graph. Record each operation, shape, precision, and conversion in the model and check it against the chosen backend’s documented coverage. Determine whether any operations fall back to the CPU and where synchronization or data conversion occurs.
- Measure the complete generation path. Include model loading or compilation, prompt or conditioning work, denoising iterations, output conversion, synchronization, texture upload, and delivery to the renderer. A fast kernel alone does not establish a fast texture-generation experience.
- Test the renderer boundary. Confirm how generated output reaches the Vulkan renderer on the target device. The cited material does not establish texture-renderer/Vulkan interop for this proposed pipeline, so treat that connection as an implementation-specific verification item.
- Benchmark on target devices before making a real-time claim. Compare complete workloads and record operator partitioning, memory use, latency, and thermal behavior, not just a model invocation in isolation.
What should a credible benchmark report?
Report the runtime and backend, device and GPU, Android version, model version, quantization format, texture or image dimensions, denoising step count, and whether timing is cold-start or warmed. Include initialization or compilation time, end-to-end latency, peak memory, CPU/GPU partitioning, and sustained performance under thermal load. Compare candidate paths on equivalent devices and workloads; include output quality and implementation complexity alongside speed, memory, power or thermal stability, and GPU-vendor compatibility.
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Define the timing boundary explicitly. For a one-shot tile, report the time until a usable tile is ready. For streamed updates, report update cadence and stalls. For a continuously evolving texture, report sustained delivery and visual quality over time. These are different products, so a single latency number without its workload and boundary is not a meaningful “real-time” result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does existing mobile diffusion research prove real-time texture synthesis?
No. Choi and coauthors’ paper “Squeezing Large-Scale Diffusion Models for Mobile,” presented at the 2023 ICML Workshop on Challenges in Deployable Generative AI, reports Mobile Stable Diffusion inference latency of less than seven seconds for one 512×512 image on Android devices with mobile GPUs. That is evidence that mobile diffusion has been studied; it is not a Vulkan-specific result, a guarantee for current phones, or a measurement of interactive texture synthesis.
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The available evidence does not establish an end-to-end quantized diffusion model running through Android Vulkan, its operator coverage, renderer interop, or a real-time benchmark on a named device. Those claims require verification for the specific model, runtime release, and hardware being targeted.
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