AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising

• URL: http://arxiv.org/abs/2406.06911v3
• Date: Thu, 26 Sep 2024 05:47:36 GMT
• Title: AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising
• Authors: Zigeng Chen, Xinyin Ma, Gongfan Fang, Zhenxiong Tan, Xinchao Wang,
• Abstract summary: AsyncDiff is a universal and plug-and-play acceleration scheme that enables model parallelism across multiple devices. For the Stable Diffusion v2.1, AsyncDiff achieves a 2.7x speedup with negligible degradation and a 4.0x speedup with only a slight reduction of 0.38 in CLIP Score. Our experiments also demonstrate that AsyncDiff can be readily applied to video diffusion models with encouraging performances.
• Score: 49.785626309848276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have garnered significant interest from the community for their great generative ability across various applications. However, their typical multi-step sequential-denoising nature gives rise to high cumulative latency, thereby precluding the possibilities of parallel computation. To address this, we introduce AsyncDiff, a universal and plug-and-play acceleration scheme that enables model parallelism across multiple devices. Our approach divides the cumbersome noise prediction model into multiple components, assigning each to a different device. To break the dependency chain between these components, it transforms the conventional sequential denoising into an asynchronous process by exploiting the high similarity between hidden states in consecutive diffusion steps. Consequently, each component is facilitated to compute in parallel on separate devices. The proposed strategy significantly reduces inference latency while minimally impacting the generative quality. Specifically, for the Stable Diffusion v2.1, AsyncDiff achieves a 2.7x speedup with negligible degradation and a 4.0x speedup with only a slight reduction of 0.38 in CLIP Score, on four NVIDIA A5000 GPUs. Our experiments also demonstrate that AsyncDiff can be readily applied to video diffusion models with encouraging performances. The code is available at https://github.com/czg1225/AsyncDiff.

Related papers

• Asynchronous Stochastic Gradient Descent with Decoupled Backpropagation and Layer-Wise Updates [1.9241821314180372]
  One major shortcoming of backpropagation is the interlocking between the forward and backward phases of the algorithm. We propose a method that parallelises SGD updates across the layers of a model by asynchronously updating them from multiple threads. We show that this approach yields close to state-of-theart results while running up to 2.97x faster than Hogwild! scaled on multiple devices.
  arXiv  Detail & Related papers  (2024-10-08T12:32:36Z)
• PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation [9.080650575731152]
  PipeInfer is a pipelined speculative acceleration technique to reduce inter-token latency and improve system utilization for single-request scenarios. PipeInfer exhibits up to a 2.15$times$ improvement in generation speed over standard speculative inference.
  arXiv  Detail & Related papers  (2024-07-16T14:52:02Z)
• DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models [44.384572903945724]
  We propose DistriFusion to tackle the problem of generating high-resolution images with diffusion models. Our method splits the model input into multiple patches and assigns each patch to a GPU. Our method can be applied to recent Stable Diffusion XL with no quality degradation and achieve up to a 6.1$times$ speedup on eight NVIDIA A100s compared to one.
  arXiv  Detail & Related papers  (2024-02-29T18:59:58Z)
• StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation [29.30999290150683]
  We introduce StreamDiffusion, a real-time diffusion pipeline for interactive image generation. Existing diffusion models are adept at creating images from text or image prompts, yet they often fall short in real-time interaction. We present a novel approach that transforms the original sequential denoising into the denoising process.
  arXiv  Detail & Related papers  (2023-12-19T18:18:33Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation [70.74377373885645]
  We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously. MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks.
  arXiv  Detail & Related papers  (2022-12-19T14:11:52Z)
• Real-time Streaming Video Denoising with Bidirectional Buffers [48.57108807146537]
  Real-time denoising algorithms are typically adopted on the user device to remove the noise involved during the shooting and transmission of video streams. Recent multi-output inference works propagate the bidirectional temporal feature with a parallel or recurrent framework. We propose a Bidirectional Streaming Video Denoising framework, to achieve high-fidelity real-time denoising for streaming videos with both past and future temporal receptive fields.
  arXiv  Detail & Related papers  (2022-07-14T14:01:03Z)
• ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech [63.780196620966905]
  We propose ProDiff, on progressive fast diffusion model for high-quality text-to-speech. ProDiff parameterizes the denoising model by directly predicting clean data to avoid distinct quality degradation in accelerating sampling. Our evaluation demonstrates that ProDiff needs only 2 iterations to synthesize high-fidelity mel-spectrograms. ProDiff enables a sampling speed of 24x faster than real-time on a single NVIDIA 2080Ti GPU.
  arXiv  Detail & Related papers  (2022-07-13T17:45:43Z)
• Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition [62.83832841523525]
  We propose a fast and accurate parallel transformer, termed Paraformer. It accurately predicts the number of output tokens and extract hidden variables. It can attain comparable performance to the state-of-the-art AR transformer, with more than 10x speedup.
  arXiv  Detail & Related papers  (2022-06-16T17:24:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.