We present a general and efficient dataflow framework for mapping Diffusion Transformer (DiT)-based models onto tiled mesh accelerators. The framework explicitly orchestrates tiling, streaming, and Direct Memory Access (DMA)-driven data movement to efficiently execute attention and large matrix multiplication (MM). We use the AMD Ryzen AI Neural Processing Unit (NPU) [1] as a representative edge-class tiled-mesh dataflow platform. In our implementation, the DiT denoising stage runs on the NPU, while the text encoder and Variational Autoencoder (VAE) decoder remain on the CPU.The iterative denoising loop dominates end-to-end latency. We therefore focus our acceleration and optimization efforts on the denoising module, while keeping the text-embedding and VAE-decoder stages on the CPU. Each denoising step reuses the same transformer backbone, and the dominant operations within a step are MM, self-attention, and MLP submodules that combine MM with elementwise nonlinear operations. The NPU is organized as a two-dimensional array of compute tiles (CTs), also referred to as AIEs. Our design partitions input matrices into tiles and maps independent tile computations across multiple AIE cores to exploit spatial parallelism.We evaluate the framework on two representative DiT-based text-to-image models, FLUX.1-schnell and Z-Image-Turbo [2], [3], and achieve end-to-end generation of a high-quality image in just over one minute. Compared with the integrated GPU (iGPU), the NPU delivers comparable generation latency while being approximately 4.4× more power efficient (package). For FLUX.1-schnell, the iGPU achieves 10.5 s/step, while the NPU achieves 13.3 s/step. For Z-Image-Turbo, the iGPU achieves 10.1 s/step, while the NPU achieves 9.8 s/step, showing that the NPU can match and slightly surpass iGPU denoising latency. These results translate into substantially improved energy efficiency for on-device image generation. Although demonstrated on a specific NPU and two representative models, the proposed dataflow framework is neither hardware-specific nor model-specific. It generalizes naturally to other diffusion-family models and mesh-based dataflow accelerators. Our results highlight the potential of spatial NPUs as energy-efficient platforms for next-generation on-device generative AI.Code Availability: The source code and setup instructions are available at: https://github.com/jia1217/vigenflow.

Stencil computation is an essential method, particularly useful for numerical simulations in areas like acoustics, heat transfer, and electromagnetism. Recent studies have utilized AMD AI Engines (AIEs) for stencil computations by configuring multiple AIE tiles within a Compute Unit to exploit task-level parallelism, achieving notable speedup through concurrent task execution. However, this setup suffers from suboptimal performance due to high memory bandwidth demands, resulting in underutilization of the available AIE tiles. This work introduces a Tile-Level Pipeline architecture designed for stencil computation that operates with constant memory bandwidth. This approach achieves linear scalability, where performance scales linearly with the number of AIE tiles, and ensures full utilization of all AIE tiles on the chip. We empirically demonstrate these benefits using AIEs.