Neodragon: Mobile Video Generation Using Diffusion Transformer

We introduce Neodragon, a text-to-video system capable of generating 2s (49 frames @24 fps) videos at a resolution of 640×1024 directly on a Qualcomm Hexagon NPU in a record ∼6.7s (7 FPS). Differing from existing transformer-based offline text-to-video generation models, Neodragon is the first to have been specifically optimised for mobile hardware to achieve efficient, low-cost, and high-fidelity video synthesis. We achieve this through four key technical contributions: (1) Replacing the original large 4.762B T5 XXL Text-Encoder with a much smaller 0.2B DT5 (DistilT5) with minimal quality loss, enabling the entire model to run without CPU offloading. This is enabled through a novel Text-Encoder Distillation procedure which uses only generative text-prompt data and does not require any image or video data. (2) Proposing an Asymmetric Decoder Distillation approach which allows us to replace the native codec-latent-VAE decoder with a more efficient one, without disturbing the generative latent-space of the video generation pipeline. (3) Pruning of MMDiT blocks within the denoiser backbone based on their relative importance, with recovery of original performance through a two-stage distillation process. (4) Reducing the NFE (Neural Functional Evaluation) requirement of the denoiser by performing step distillation using a technique adapted from DMD for pyramidal flow-matching, thereby significantly accelerating video generation. When paired with an optimised SSD1B first-frame image generator and QuickSRNet for 2× super-resolution, our end-to-end Neodragon system becomes a highly parameter (4.945B full model), memory (3.5GB peak RAM usage), and runtime (6.7s E2E latency) efficient mobile-friendly model, while achieving a VBench total score of 81.61, yielding high-fidelity generated videos. By enabling low-cost, private, and on-device text-to-video synthesis, Neodragon democratizes AI-based video content creation, empowering creators to generate high-quality videos without reliance on cloud services. Code and model will be made publicly available at our website.