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Yanyu Li

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15 papers

2 author rows

ICML Conference 2025 Conference Paper

Improving the Diffusability of Autoencoders

Ivan Skorokhodov
Sharath Girish
Benran Hu 0001
Willi Menapace
Yanyu Li
Rameen Abdal
Sergey Tulyakov
Aliaksandr Siarohin

Latent diffusion models have emerged as the leading approach for generating high-quality images and videos, utilizing compressed latent representations to reduce the computational burden of the diffusion process. While recent advancements have primarily focused on scaling diffusion backbones and improving autoencoder reconstruction quality, the interaction between these components has received comparatively less attention. In this work, we perform a spectral analysis of modern autoencoders and identify inordinate high-frequency components in their latent spaces, which are especially pronounced in the autoencoders with a large bottleneck channel size. We hypothesize that this high-frequency component interferes with the coarse-to-fine nature of the diffusion synthesis process and hinders the generation quality. To mitigate the issue, we propose scale equivariance: a simple regularization strategy that aligns latent and RGB spaces across frequencies by enforcing scale equivariance in the decoder. It requires minimal code changes and only up to $20$K autoencoder fine-tuning steps, yet significantly improves generation quality, reducing FID by 19% for image generation on ImageNet-1K 256x256 and FVD by at least 44% for video generation on Kinetics-700 17x256x256. The source code is available at https: //github. com/snap-research/diffusability.

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AAAI Conference 2025 Conference Paper

LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers

Xuan Shen
Zhao Song
Yufa Zhou
Bo Chen
Yanyu Li
Yifan Gong
Kai Zhang
Hao Tan

Diffusion Transformers have emerged as the preeminent models for a wide array of generative tasks, demonstrating superior performance and efficacy across various applications. The promising results come at the cost of slow inference, as each denoising step requires running the whole transformer model with a large amount of parameters. In this paper, we show that performing the full computation of the model at each diffusion step is unnecessary, as some computations can be skipped by lazily reusing the results of previous steps. Furthermore, we show that the lower bound of similarity between outputs at consecutive steps is notably high, and this similarity can be linearly approximated using the inputs. To verify our demonstrations, we propose the **LazyDiT**, a lazy learning framework that efficiently leverages cached results from earlier steps to skip redundant computations. Specifically, we incorporate lazy learning layers into the model, effectively trained to maximize laziness, enabling dynamic skipping of redundant computations. Experimental results show that LazyDiT outperforms the DDIM sampler across multiple diffusion transformer models at various resolutions. Furthermore, we implement our method on mobile devices, achieving better performance than DDIM with similar latency.

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NeurIPS Conference 2024 Conference Paper

BitsFusion: 1.99 bits Weight Quantization of Diffusion Model

Yang Sui
Yanyu Li
Anil Kag
Yerlan Idelbayev
Junli Cao
Ju Hu
Dhritiman Sagar
Bo Yuan

Diffusion-based image generation models have achieved great success in recent years by showing the capability of synthesizing high-quality content. However, these models contain a huge number of parameters, resulting in a significantly large model size. Saving and transferring them is a major bottleneck for various applications, especially those running on resource-constrained devices. In this work, we develop a novel weight quantization method that quantizes the UNet from Stable Diffusion v1. 5 to $1. 99$ bits, achieving a model with $7. 9\times$ smaller size while exhibiting even better generation quality than the original one. Our approach includes several novel techniques, such as assigning optimal bits to each layer, initializing the quantized model for better performance, and improving the training strategy to dramatically reduce quantization error. Furthermore, we extensively evaluate our quantized model across various benchmark datasets and through human evaluation to demonstrate its superior generation quality.

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ICML Conference 2024 Conference Paper

E2GAN: Efficient Training of Efficient GANs for Image-to-Image Translation

Yifan Gong 0004
Zheng Zhan 0001
Qing Jin
Yanyu Li
Yerlan Idelbayev
Xian Liu
Andrey Zharkov
Kfir Aberman

One highly promising direction for enabling flexible real-time on-device image editing is utilizing data distillation by leveraging large-scale text-to-image diffusion models to generate paired datasets used for training generative adversarial networks (GANs). This approach notably alleviates the stringent requirements typically imposed by high-end commercial GPUs for performing image editing with diffusion models. However, unlike text-to-image diffusion models, each distilled GAN is specialized for a specific image editing task, necessitating costly training efforts to obtain models for various concepts. In this work, we introduce and address a novel research direction: can the process of distilling GANs from diffusion models be made significantly more efficient? To achieve this goal, we propose a series of innovative techniques. First, we construct a base GAN model with generalized features, adaptable to different concepts through fine-tuning, eliminating the need for training from scratch. Second, we identify crucial layers within the base GAN model and employ Low-Rank Adaptation (LoRA) with a simple yet effective rank search process, rather than fine-tuning the entire base model. Third, we investigate the minimal amount of data necessary for fine-tuning, further reducing the overall training time. Extensive experiments show that we can efficiently empower GANs with the ability to perform real-time high-quality image editing on mobile devices with remarkably reduced training and storage costs for each concept.

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IJCAI Conference 2024 Conference Paper

FasterVD: On Acceleration of Video Diffusion Models

Pinrui Yu
Dan Luo
Timothy Rupprecht
Lei Lu
Zhenglun Kong
Pu Zhao
Yanyu Li
Octavia Camps

Equipped with Denoising Diffusion Probabilistic Models, video content generation has gained significant research interest recently. However, diffusion pipelines call for intensive computation and model storage, which poses challenges for their wide and efficient deployment. In this work, we address this issue by integrating LCM-LoRA to reduce the denoising steps and escalating the video generation process by frame skipping and interpolation. Our framework achieves an approximately 10× inference acceleration for high-quality realistic video generation on commonly available GPUs.

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ICLR Conference 2024 Conference Paper

HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion

Xian Liu
Jian Ren 0005
Aliaksandr Siarohin
Ivan Skorokhodov
Yanyu Li
Dahua Lin
Xihui Liu
Ziwei Liu 0002

Despite significant advances in large-scale text-to-image models, achieving hyper-realistic human image generation remains a desirable yet unsolved task. Existing models like Stable Diffusion and DALL·E 2 tend to generate human images with incoherent parts or unnatural poses. To tackle these challenges, our key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. To this end, we propose a unified framework, HyperHuman, that generates in-the-wild human images of high realism and diverse layouts. Specifically, 1) we first build a large-scale human-centric dataset, named HumanVerse, which consists of 340M images with comprehensive annotations like human pose, depth, and surface normal. 2) Next, we propose a Latent Structural Diffusion Model that simultaneously denoises the depth and surface normal along with the synthesized RGB image. Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network, where each branch in the model complements to each other with both structural awareness and textural richness. 3) Finally, to further boost the visual quality, we propose a Structure-Guided Refiner to compose the predicted conditions for more detailed generation of higher resolution. Extensive experiments demonstrate that our framework yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios.

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NeurIPS Conference 2024 Conference Paper

SF-V: Single Forward Video Generation Model

Zhixing Zhang
Yanyu Li
Yushu Wu
Yanwu Xu
Anil Kag
Ivan Skorokhodov
Willi Menapace
Aliaksandr Siarohin

Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process. However, these models require multiple denoising steps during sampling, resulting in high computational costs. In this work, we propose a novel approach to obtain single-step video generation models by leveraging adversarial training to fine-tune pre-trained video diffusion models. We show that, through the adversarial training, the multi-steps video diffusion model, i. e. , Stable Video Diffusion (SVD), can be trained to perform single forward pass to synthesize high-quality videos, capturing both temporal and spatial dependencies in the video data. Extensive experiments demonstrate that our method achieves competitive generation quality of synthesized videos with significantly reduced computational overhead for the denoising process (i. e. , around $23\times$ speedup compared with SVD and $6\times$ speedup compared with existing works, with even better generation quality), paving the way for real-time video synthesis and editing.

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NeurIPS Conference 2023 Conference Paper

SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds

Yanyu Li
Huan Wang
Qing Jin
Ju Hu
Pavlo Chemerys
Yun Fu
Yanzhi Wang
Sergey Tulyakov

Text-to-image diffusion models can create stunning images from natural language descriptions that rival the work of professional artists and photographers. However, these models are large, with complex network architectures and tens of denoising iterations, making them computationally expensive and slow to run. As a result, high-end GPUs and cloud-based inference are required to run diffusion models at scale. This is costly and has privacy implications, especially when user data is sent to a third party. To overcome these challenges, we present a generic approach that, for the first time, unlocks running text-to-image diffusion models on mobile devices in **less than 2 seconds**. We achieve so by introducing efficient network architecture and improving step distillation. Specifically, we propose an efficient UNet by identifying the redundancy of the original model and reducing the computation of the image decoder via data distillation. Further, we enhance the step distillation by exploring training strategies and introducing regularization from classifier-free guidance. Our extensive experiments on MS-COCO show that our model with $8$ denoising steps achieves better FID and CLIP scores than Stable Diffusion v$1. 5$ with $50$ steps. Our work democratizes content creation by bringing powerful text-to-image diffusion models to the hands of users.

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AAAI Conference 2023 Conference Paper

Towards Real-Time Segmentation on the Edge

Yanyu Li
Changdi Yang
Pu Zhao
Geng Yuan
Wei Niu
Jiexiong Guan
Hao Tang
Minghai Qin

The research in real-time segmentation mainly focuses on desktop GPUs. However, autonomous driving and many other applications rely on real-time segmentation on the edge, and current arts are far from the goal. In addition, recent advances in vision transformers also inspire us to re-design the network architecture for dense prediction task. In this work, we propose to combine the self attention block with lightweight convolutions to form new building blocks, and employ latency constraints to search an efficient sub-network. We train an MLP latency model based on generated architecture configurations and their latency measured on mobile devices, so that we can predict the latency of subnets during search phase. To the best of our knowledge, we are the first to achieve over 74% mIoU on Cityscapes with semi-real-time inference (over 15 FPS) on mobile GPU from an off-the-shelf phone.

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NeurIPS Conference 2022 Conference Paper

EfficientFormer: Vision Transformers at MobileNet Speed

Yanyu Li
Geng Yuan
Yang Wen
Ju Hu
Georgios Evangelidis
Sergey Tulyakov
Yanzhi Wang
Jian Ren

Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks. However, due to the massive number of parameters and model design, e. g. , attention mechanism, ViT-based models are generally times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance? To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs. Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm. Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer. Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices. Our fastest model, EfficientFormer-L1, achieves $79. 2\%$ top-1 accuracy on ImageNet-1K with only $1. 6$ ms inference latency on iPhone 12 (compiled with CoreML), which runs as fast as MobileNetV2$\times 1. 4$ ($1. 6$ ms, $74. 7\%$ top-1), and our largest model, EfficientFormer-L7, obtains $83. 3\%$ accuracy with only $7. 0$ ms latency. Our work proves that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance.

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NeurIPS Conference 2022 Conference Paper

Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training

Geng Yuan
Yanyu Li
Sheng Li
Zhenglun Kong
Sergey Tulyakov
Xulong Tang
Yanzhi Wang
Jian Ren

Recently, sparse training has emerged as a promising paradigm for efficient deep learning on edge devices. The current research mainly devotes the efforts to reducing training costs by further increasing model sparsity. However, increasing sparsity is not always ideal since it will inevitably introduce severe accuracy degradation at an extremely high sparsity level. This paper intends to explore other possible directions to effectively and efficiently reduce sparse training costs while preserving accuracy. To this end, we investigate two techniques, namely, layer freezing and data sieving. First, the layer freezing approach has shown its success in dense model training and fine-tuning, yet it has never been adopted in the sparse training domain. Nevertheless, the unique characteristics of sparse training may hinder the incorporation of layer freezing techniques. Therefore, we analyze the feasibility and potentiality of using the layer freezing technique in sparse training and find it has the potential to save considerable training costs. Second, we propose a data sieving method for dataset-efficient training, which further reduces training costs by ensuring only a partial dataset is used throughout the entire training process. We show that both techniques can be well incorporated into the sparse training algorithm to form a generic framework, which we dub SpFDE. Our extensive experiments demonstrate that SpFDE can significantly reduce training costs while preserving accuracy from three dimensions: weight sparsity, layer freezing, and dataset sieving. Our code and models will be released.

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IJCAI Conference 2022 Conference Paper

Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization

Yanyu Li
Pu Zhao
Geng Yuan
Xue Lin
Yanzhi Wang
Xin Chen

Neural architecture search (NAS) and network pruning are widely studied efficient AI techniques, but not yet perfect. NAS performs exhaustive candidate architecture search, incurring tremendous search cost. Though (structured) pruning can simply shrink model dimension, it remains unclear how to decide the per-layer sparsity automatically and optimally. In this work, we revisit the problem of layer-width optimization and propose Pruning-as-Search (PaS), an end-to-end channel pruning method to search out desired sub-network automatically and efficiently. Specifically, we add a depth-wise binary convolution to learn pruning policies directly through gradient descent. By combining the structural reparameterization and PaS, we successfully searched out a new family of VGG-like and lightweight networks, which enable the flexibility of arbitrary width with respect to each layer instead of each stage. Experimental results show that our proposed architecture outperforms prior arts by around 1. 0% top-1 accuracy under similar inference speed on ImageNet-1000 classification task. Furthermore, we demonstrate the effectiveness of our width search on complex tasks including instance segmentation and image translation. Code and models are released.

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IJCAI Conference 2022 Conference Paper

Real-Time Portrait Stylization on the Edge

Yanyu Li
Xuan Shen
Geng Yuan
Jiexiong Guan
Wei Niu
Hao Tang
Bin Ren
Yanzhi Wang

In this work we demonstrate real-time portrait stylization, specifically, translating self-portrait into cartoon or anime style on mobile devices. We propose a latency-driven differentiable architecture search method, maintaining realistic generative quality. With our framework, we obtain 10× computation reduction on the generative model and achieve real-time video stylization on off-the-shelf smartphone using mobile GPUs.

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AAAI Conference 2021 System Paper

A Compression-Compilation Co-Design Framework Towards Real-Time Object Detection on Mobile Devices

Yuxuan Cai
Geng Yuan
Hongjia Li
Wei Niu
Yanyu Li
Xulong Tang
Bin Ren
Yanzhi Wang

The rapid development and wide utilization of object detection techniques have aroused requirements for both accuracy and speed of object detectors. In this work, we propose a compression-compilation co-design framework to achieve real-time YOLOv4 inference on mobile devices. We propose a novel fine-grained structured pruning, which maintain high accuracy while achieving high hardware parallelism. Our pruned YOLOv4 achieves 48. 9 mAP and 17 FPS inference speed on an off-the-shelf Samsung Galaxy S20 smartphone, which is 5. 5× faster than the original state-of-the-art detector YOLOv4.

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AAAI Conference 2021 Conference Paper

YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design

Yuxuan Cai
Hongjia Li
Geng Yuan
Wei Niu
Yanyu Li
Xulong Tang
Bin Ren
Yanzhi Wang

The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-theart object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14× compression rate of YOLOv4 with 49. 0 mAP. Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme, the inference speed is increased to 19. 1 FPS, and outperforms the original YOLOv4 by 5× speedup. Source code is at: https: //github. com/nightsnack/YOLObile.

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