stable-diffusion.cpp: Lightweight Inference

• stable-diffusion.cpp is a lightweight C/C++ inference engine for Stable Diffusion that leverages the GGML tensor library for low-overhead, portable deployment.
• The framework optimizes the convolution operator using the Winograd algorithm to reduce latency and enable precise evaluation of operator-level costs.
• It serves as a benchmark for accelerator research and supports runtime-aware optimizations across diverse platforms including Mac, Android, and AMD devices.

stable-diffusion.cpp, often abbreviated Sdcpp, is an open-source, lightweight, pure C/C++ inference framework for Stable Diffusion built on GGML. It is designed for CPU inference and can also run on GPUs with the right backend configuration. In the recent systems literature, it occupies a distinctive position as both a deployable inference stack and an experimental substrate for studying low-overhead generative-model execution, because its GGML-based implementation exposes operator behavior, quantization formats, and runtime effects more directly than heavier frameworks (Ng et al., 2024, Ando et al., 4 Nov 2025).

1. Definition and software position

Sdcpp is described as a C/C++ implementation of Stable Diffusion v1.5 based on the GGML tensor library. GGML is used because it is lightweight, low-overhead, hardware-friendly, and supports quantized computation well. In this sense, stable-diffusion.cpp serves not only as an inference engine but also as a systems benchmark in which operator-level costs can be studied without the software overhead of frameworks like PyTorch (Ando et al., 4 Nov 2025).

Within diffusion inference, the central computational burden is the iterative denoising loop, especially in the U-Net, which consumes most of the compute. Sdcpp is therefore structurally aligned with the dominant kernels of latent diffusion inference: repeated convolution-heavy and dot-product-heavy operations during sampling. This makes it a useful platform for both software optimization and hardware co-design.

The framework is also characterized as intentionally deployable. The optimized implementation discussed in recent work remains a dependency-light C/C++ framework and is validated across multiple platforms, including Mac (Metal/GPU), Android (OpenCL/Qualcomm), and AMD devices. A plausible implication is that stable-diffusion.cpp is especially relevant in settings where portability and low framework overhead are primary engineering constraints rather than maximal throughput on a single specialized backend (Ng et al., 2024).

2. Computational structure and the ggml_conv_2d bottleneck

A central observation in the current literature is that Sdcpp’s original ggml_conv_2d operator is a major performance and memory bottleneck. Because Stable Diffusion pipelines repeatedly invoke convolution-heavy U-Net blocks during sampling, a slow and memory-hungry 2D convolution implementation directly dominates end-to-end latency (Ng et al., 2024).

The optimization work on Sdcpp treats the convolution operator as the primary target for acceleration and frames the improved implementation around the Winograd algorithm for 2D convolution. Instead of a conventional direct convolution, the computation is decomposed into three stages: (i) preprocessing of filter and activation weights, (ii) element-wise multiplication between the preprocessed tensors, and (iii) postprocessing of the intermediate results. In common Winograd notation, the transformed computation is represented as

$Y = A^T \left[(G g G^T) \odot (B^T d B)\right] A$

where $g$ is the filter, $d$ is the input tile, and $\odot$ is elementwise multiplication (Ng et al., 2024).

This decomposition matters because it changes the dominant cost profile of convolution. The optimized operator is not presented as a generic algebraic replacement alone; rather, it is embedded in the broader Sdcpp runtime so that preprocessing, tiled multiplication, and postprocessing are coordinated with the framework’s execution model. A common misconception is that the acceleration comes only from introducing Winograd transforms. The published description is more specific: the improvement depends on the interaction between transform-based convolution, locality-aware data layout, and runtime scheduling (Ng et al., 2024).

3. Runtime-aware optimization and operator support

The optimized Sdcpp implementation analyzes both the dependent and independent computation graphs inside the Winograd pipeline. That analysis is used to improve both locality and parallelism. For locality, the implementation applies scatter-store and gather-load optimizations, arranging data so that the working set fits better in L1 cache during loading and reducing cache swapping. For parallelism, it exploits the fact that many Winograd sub-operations are independent and can be distributed across threads and cores (Ng et al., 2024).

This runtime behavior is especially significant on heterogeneous Apple Silicon systems. The original GGML-style runtime is described as using a more static shared-state approach in which cores may be assigned the same workload pattern. In the new design, when a core finishes a block, it can dynamically receive the next block, which better balances heterogeneous cores, particularly systems with both P-cores and E-cores (Ng et al., 2024).

The implementation also broadens operator coverage. Earlier versions had unsupported operators on Android, causing incorrect results for models such as SDXL. The new implementation supports the needed operators across multiple Stable Diffusion variants and devices, including Mac, Android, and AMD. It also notes support for quantization and for operators used in diffusion-transformer-style models such as those appearing in Open-Sora-type video generation pipelines (Ng et al., 2024).

Correctness is treated as an end-to-end property rather than only a kernel-level property. The optimized framework is reported to deliver correct end-to-end results across SDv1.4, v1.5, v2.1, SDXL, and SDXL-Turbo. It also mentions support for variants like Realistic Vision and arbitrary user-specified LoRA modules. This suggests that the optimization effort is aimed at preserving compatibility with practical Stable Diffusion workflows rather than only accelerating a narrow benchmark path (Ng et al., 2024).

4. Empirical performance and platform coverage

The reported evaluation emphasizes Mac systems with Apple Silicon, specifically an M1 Pro machine with 16 GB memory running macOS Sonoma 15.1, and an M2 Max machine with 32 GB memory running macOS Sequoia 15.0. Successful testing is also reported on Android devices using Qualcomm + OpenCL, and on Mac GPU via Metal (Ng et al., 2024).

For individual convolution layers, the paper reports speedups above 2× across various layer configurations in the sampling process; the abstract summarizes this as “up to 2.76×” for single convolution layers. For end-to-end image generation, the strongest gains occur at larger image sizes, where convolution accounts for a larger fraction of total inference cost (Ng et al., 2024).

Setup	M1 Pro	M2 Max
SDXL, 1024×1024, 20 steps	4.79× FP32, 3.06× FP16	4.64× FP32, 3.12× FP16
SDv2, 768×768, 20 steps	2.02× FP32, 1.68× FP16	1.95× FP32, 1.73× FP16
SDv1.5, 512×512, 20 steps	1.84× FP32, 1.51× FP16	1.77× FP32, 1.53× FP16

These results are accompanied by two explicit claims. First, the optimized implementation preserves correct end-to-end outputs across the tested Stable Diffusion models, including SDXL and SDXL-Turbo. Second, the improvements are especially significant for larger images and heavier models such as SDXL, where convolution is a larger fraction of total inference cost. The pattern indicates that Sdcpp’s bottlenecks are model- and resolution-sensitive rather than uniform across all workloads (Ng et al., 2024).

5. stable-diffusion.cpp as a benchmark for accelerator research

Beyond software optimization, stable-diffusion.cpp has been used as the benchmark framework in accelerator studies. In work on IMAX3, a general-purpose Coarse-Grained Linear Array (CGLA)/CGRA accelerator, the framework is used because its GGML-based structure is lightweight and hardware-friendly, and because it exposes quantized computation paths that are amenable to accelerator mapping (Ando et al., 4 Nov 2025).

That study does not offload the full model. Instead, it targets the quantized dot-product kernels that form the arithmetic core of the U-Net’s convolution and attention operations, specifically the Q8_0 and Q3_K formats. The reported dot-product execution-time breakdown makes clear why partial offload limits end-to-end speedup: for the Q3_K model, execution time is 30.7% F32, 59.0% F16, and 10.3% Q3_K; for the Q8_0 model, it is 21.8% F32, 62.0% F16, and 16.3% Q8_0. The paper explicitly states that the actual quantized kernels offloaded to IMAX3 account for less than 20% of the total dot-product time in these runs, because a large share remains in F32/F16 on the host CPU (Ando et al., 4 Nov 2025).

The resulting end-to-end latencies illustrate this limitation. For a Q3_K model, image-generation latency is 790.3 s on FPGA IMAX3, 809.7 s on the standalone ARM CPU, 754.5 s on the projected 840 MHz ASIC IMAX3, 59.3 s on the Xeon CPU, and 16.2 s on the GPU. For a Q8_0 model, latency is 654.7 s on FPGA IMAX3, 625.1 s on the standalone ARM CPU, and 558.0 s on the projected ASIC IMAX3. The authors therefore distinguish clearly between kernel-level competitiveness and end-to-end competitiveness (Ando et al., 4 Nov 2025).

The same work also uses Power-Delay Product (PDP) as the energy-efficiency metric, with

$PDP = \text{Execution Time} \times \text{Power}$

and reports that the projected ASIC IMAX3 significantly outperforms the Xeon CPU in PDP for both Q3_K and Q8_0, while in the Q3_K case it has a lower PDP than the GPU. Performance improves well up to 2 lanes, but shows diminishing returns at 3 or more lanes, attributed to the dual-core host CPU bottleneck. For stable-diffusion.cpp, the broader lesson is that operator offload, host-side orchestration, and memory traffic must be co-optimized; accelerator throughput alone does not determine end-to-end behavior (Ando et al., 4 Nov 2025).

6. Privacy-preserving and split-execution extensions

A different line of work considers privacy-preserving diffusion inference and is relevant to stable-diffusion.cpp chiefly as a deployment pattern rather than as an ordinary performance optimization. HE-Diffusion introduces a privacy-preserving stable diffusion framework leveraging homomorphic encryption, focusing on the denoising phase and protecting the sensitive intermediate latent state $x_i$. The denoising relation is written as

$x_{i+1} = f(\alpha_i, e_i, x_i)$

and the HE-friendly reformulation moves most arithmetic that does not depend on the encrypted latent into plaintext, reducing the encrypted path to a multiply-add on the encrypted latent (Chen et al., 2024).

The system uses CKKS via TenSEAL, assumes an honest but curious server, and introduces min-distortion partial image encryption together with a COO sparse tensor representation. It reports a 500× speedup relative to the naive encrypted baseline, reducing encrypted inference to the minute level while claiming that performance and accuracy are on par with the plaintext counterpart. The quantitative similarity metrics reported between plaintext and encrypted outputs are Cosine similarity 0.9818, SSIM 0.9995, PSNR 68.65, MSE 0.0089, and KL divergence 0.0046 (Chen et al., 2024).

For stable-diffusion.cpp, the significance of this work is carefully delimited in the source material. It explicitly states that a classic stable-diffusion.cpp stack is a plaintext inference project optimized for CPU/GGML-style deployment, not for encrypted inference. It also states that the paper is more relevant as a privacy-preserving system design reference than as a direct performance optimization recipe for normal Stable Diffusion inference. The most transferable ideas are therefore architectural rather than intrinsic to Sdcpp: split the pipeline so only the sensitive latent path is protected, algebraically rewrite denoising to minimize encrypted operations, use sparse latent representations and only process nonzeros, and move any serious implementation from Python/TenSEAL toward a C++ backend if practical throughput is required (Chen et al., 2024).

Taken together, these lines of work place stable-diffusion.cpp at the intersection of three systems concerns: low-overhead portable inference, operator- and runtime-level optimization, and deployment-oriented experimentation in hardware acceleration and privacy-preserving execution. The framework’s importance in current research lies less in novel model architecture than in its role as a compact, inspectable, and modifiable substrate for studying how Stable Diffusion behaves under real implementation constraints.