Diffusion Transformers have demonstrated remarkable capabilities in image generation but often come with excessive parameterization, resulting in considerable inference overhead in real-world applications. In this work, we present TinyFusion, a depth pruning method designed to remove redundant layers from diffusion transformers via end-to-end learning. The core principle of our approach is to create a pruned model with high recoverability, allowing it to regain strong performance after fine-tuning. To accomplish this, we introduce a differentiable sampling technique to make pruning learnable, paired with a co-optimized parameter to simulate future fine-tuning. While prior works focus on minimizing loss or error after pruning, our method explicitly models and optimizes the post-fine-tuning performance of pruned models. Experimental results indicate that this learnable paradigm offers substantial benefits for layer pruning of diffusion transformers, surpassing existing importance-based and error-based methods. Additionally, TinyFusion exhibits strong generalization across diverse architectures, such as DiTs, MARs, and SiTs. Experiments with DiT-XL show that TinyFusion can craft a shallow diffusion transformer at less than 7% of the pre-training cost, achieving a 2times speedup with an FID score of 2.86, outperforming competitors with comparable efficiency.

A confluence of advances in several fields has led to the emergence of mixed-reality headsets. They can enable us to interact with and visualize our environments in novel ways. Nonetheless, mixed-reality headsets are constrained today as their camera systems only capture a narrow part of the visible spectrum. Our environment contains rich information that cameras do not capture. It includes phenomena captured through sensors, electromagnetic fields beyond visible light, acoustic emissions, and magnetic fields. We demonstrate our ongoing work, PixelGen, to redesign cameras for low power consumption and to be able to visualize our environments in a novel manner, making some of the invisible phenomena visible. Pixel-Gen combines low-bandwidth sensors with a monochrome camera to capture a rich representation of the world. This design choice ensures information is communicated energy-efficiently. This information is then combined with diffusion-based image models to generate unique representations of the environment, visualizing the otherwise invisible fields. We demonstrate that together with a mixed reality headset, it enables us to observe the world uniquely.