search
HomeTechnology peripheralsAIApple's large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

I am used to Stable Diffusion, and now I finally have a Matryoshka-style Diffusion model, made by Apple.

#In the era of generative AI, diffusion models have become a popular tool for generative AI applications such as image, video, 3D, audio, and text generation. However, extending diffusion models to high-resolution domains still faces significant challenges because the model must recode all high-resolution inputs at each step. Solving these challenges requires the use of deep architectures with attention blocks, which makes optimization more difficult and consumes more computing power and memory.

How to do it? Some recent work has focused on investigating efficient network architectures for high-resolution images. However, none of the existing methods have demonstrated results beyond 512×512 resolution, and the generation quality lags behind mainstream cascade or latent methods.

We take OpenAI DALL-E 2, Google IMAGEN and NVIDIA eDiffI as examples. They save computation by learning a low-resolution model and multiple super-resolution diffusion models. force, where each component is trained individually. On the other hand, the latent diffusion model (LDM) only learns a low-resolution diffusion model and relies on a separately trained high-resolution autoencoder. For both solutions, multi-stage pipelines complicate training and inference, often requiring careful tuning or hyperparameters.

In this article, researchers propose the Matryoshka Diffusion Models (MDM), which is a new diffusion model for end-to-end high-resolution image generation. Model. The code will be released soon.

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Paper address: https://arxiv.org/pdf/2310.15111.pdf

The research proposed The main idea is to use the low-resolution diffusion process as part of the high-resolution generation by performing a joint diffusion process at multiple resolutions using a nested UNet architecture.

The study found that: MDM and nested UNet architecture together achieve 1) multi-resolution loss: greatly improving the convergence speed of high-resolution input denoising; 2) Efficient progressive training plan, starting from training a low-resolution diffusion model and gradually adding high-resolution inputs and outputs according to the plan. Experimental results show that combining multi-resolution loss with progressive training can achieve a better balance between training cost and model quality.

This study evaluates MDM on class-conditional image generation as well as text-conditional image and video generation. MDM allows training high-resolution models without the use of cascades or latent diffusion. Ablation studies show that both multi-resolution loss and progressive training greatly improve training efficiency and quality.

Let’s enjoy the following pictures and videos generated by MDM.
Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Method Overview

Researcher Introduction The MDM diffusion model is trained end-to-end in high resolution while leveraging hierarchically structured data formation. MDM first generalizes the standard diffusion model in diffusion space and then proposes a dedicated nested architecture and training process.

First let’s look at how to generalize the standard diffusion model in the extended space.

The difference from cascade or latent methods is that MDM learns a single diffusion process with a hierarchical structure by introducing multi-resolution diffusion processes in an expansion space . The details are shown in Figure 2 below.

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Specifically, given a data point x ∈ R^N, the researcher defines a time-related latent variable z_t = z_t^1, . . . , z_t^R ∈ R^N_1...NR.

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Researchers say that conducting diffusion modeling in extended space has the following two advantages. For one, we typically care about the full-resolution output z_t^R during inference, then all other intermediate resolutions are treated as additional latent variables z_t^r, increasing the complexity of modeling the distribution. Second, multi-resolution dependencies provide the opportunity to share weights and computations across z_t^r, thereby redistributing computation in a more efficient manner and enabling efficient training and inference.

Let’s see how nested architecture (NestedUNet) works.

Similar to typical diffusion models, researchers use a UNet network structure to implement MDM, where residual connections and computational blocks are used in parallel to preserve fine-grained input information. The computational block here contains multiple layers of convolution and self-attention layers. The codes for NestedUNet and standard UNet are as follows.

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

In addition to its simplicity compared to other hierarchical methods, NestedUNet allows calculations to be distributed in the most efficient way. As shown in Figure 3 below, early exploration by researchers found that MDM achieves significantly better scalability when allocating most parameters and calculations at the lowest resolution.

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Finally learn.

Researchers use conventional denoising targets to train MDM at multiple resolutions, as shown in equation (3) below.

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Progressive training is used here. The researchers directly trained MDM end-to-end according to the above formula (3) and demonstrated better convergence than the original baseline method. They found that using a simple progressive training method similar to that proposed in the GAN paper greatly accelerated the training of high-resolution models.

This training method avoids costly high-resolution training from the beginning and accelerates overall convergence. Not only that, they also incorporated mixed-resolution training, which trains samples with different final resolutions simultaneously in a single batch.

Experiments and results

##MDM is a general technology that can be gradually Any problems compressing input dimensions. A comparison of MDM with the baseline approach is shown in Figure 4 below.

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Table 1 gives the comparison results on ImageNet (FID-50K) and COCO (FID-30K).

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Figures 5, 6 and 7 below show the performance of MDM in image generation (Figure 5), text to image (Figure 6) and text to video (Figure 7) result. Despite being trained on a relatively small dataset, MDM shows strong zero-shot ability to generate high-resolution images and videos.

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Apples large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution

Interested readers can read the original text of the paper to learn more about the research content.

The above is the detailed content of Apple's large Vincent picture model unveiled: Russian matryoshka-like spread, supporting 1024x1024 resolution. For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:机器之心. If there is any infringement, please contact admin@php.cn delete
DSA如何弯道超车NVIDIA GPU?DSA如何弯道超车NVIDIA GPU?Sep 20, 2023 pm 06:09 PM

你可能听过以下犀利的观点:1.跟着NVIDIA的技术路线,可能永远也追不上NVIDIA的脚步。2.DSA或许有机会追赶上NVIDIA,但目前的状况是DSA濒临消亡,看不到任何希望另一方面,我们都知道现在大模型正处于风口位置,业界很多人想做大模型芯片,也有很多人想投大模型芯片。但是,大模型芯片的设计关键在哪,大带宽大内存的重要性好像大家都知道,但做出来的芯片跟NVIDIA相比,又有何不同?带着问题,本文尝试给大家一点启发。纯粹以观点为主的文章往往显得形式主义,我们可以通过一个架构的例子来说明Sam

阿里云通义千问14B模型开源!性能超越Llama2等同等尺寸模型阿里云通义千问14B模型开源!性能超越Llama2等同等尺寸模型Sep 25, 2023 pm 10:25 PM

2021年9月25日,阿里云发布了开源项目通义千问140亿参数模型Qwen-14B以及其对话模型Qwen-14B-Chat,并且可以免费商用。Qwen-14B在多个权威评测中表现出色,超过了同等规模的模型,甚至有些指标接近Llama2-70B。此前,阿里云还开源了70亿参数模型Qwen-7B,仅一个多月的时间下载量就突破了100万,成为开源社区的热门项目Qwen-14B是一款支持多种语言的高性能开源模型,相比同类模型使用了更多的高质量数据,整体训练数据超过3万亿Token,使得模型具备更强大的推

ICCV 2023揭晓:ControlNet、SAM等热门论文斩获奖项ICCV 2023揭晓:ControlNet、SAM等热门论文斩获奖项Oct 04, 2023 pm 09:37 PM

在法国巴黎举行了国际计算机视觉大会ICCV(InternationalConferenceonComputerVision)本周开幕作为全球计算机视觉领域顶级的学术会议,ICCV每两年召开一次。ICCV的热度一直以来都与CVPR不相上下,屡创新高在今天的开幕式上,ICCV官方公布了今年的论文数据:本届ICCV共有8068篇投稿,其中有2160篇被接收,录用率为26.8%,略高于上一届ICCV2021的录用率25.9%在论文主题方面,官方也公布了相关数据:多视角和传感器的3D技术热度最高在今天的开

复旦大学团队发布中文智慧法律系统DISC-LawLLM,构建司法评测基准,开源30万微调数据复旦大学团队发布中文智慧法律系统DISC-LawLLM,构建司法评测基准,开源30万微调数据Sep 29, 2023 pm 01:17 PM

随着智慧司法的兴起,智能化方法驱动的智能法律系统有望惠及不同群体。例如,为法律专业人员减轻文书工作,为普通民众提供法律咨询服务,为法学学生提供学习和考试辅导。由于法律知识的独特性和司法任务的多样性,此前的智慧司法研究方面主要着眼于为特定任务设计自动化算法,难以满足对司法领域提供支撑性服务的需求,离应用落地有不小的距离。而大型语言模型(LLMs)在不同的传统任务上展示出强大的能力,为智能法律系统的进一步发展带来希望。近日,复旦大学数据智能与社会计算实验室(FudanDISC)发布大语言模型驱动的中

百度文心一言全面向全社会开放,率先迈出重要一步百度文心一言全面向全社会开放,率先迈出重要一步Aug 31, 2023 pm 01:33 PM

8月31日,文心一言首次向全社会全面开放。用户可以在应用商店下载“文心一言APP”或登录“文心一言官网”(https://yiyan.baidu.com)进行体验据报道,百度计划推出一系列经过全新重构的AI原生应用,以便让用户充分体验生成式AI的理解、生成、逻辑和记忆等四大核心能力今年3月16日,文心一言开启邀测。作为全球大厂中首个发布的生成式AI产品,文心一言的基础模型文心大模型早在2019年就在国内率先发布,近期升级的文心大模型3.5也持续在十余个国内外权威测评中位居第一。李彦宏表示,当文心

AI技术在蚂蚁集团保险业务中的应用:革新保险服务,带来全新体验AI技术在蚂蚁集团保险业务中的应用:革新保险服务,带来全新体验Sep 20, 2023 pm 10:45 PM

保险行业对于社会民生和国民经济的重要性不言而喻。作为风险管理工具,保险为人民群众提供保障和福利,推动经济的稳定和可持续发展。在新的时代背景下,保险行业面临着新的机遇和挑战,需要不断创新和转型,以适应社会需求的变化和经济结构的调整近年来,中国的保险科技蓬勃发展。通过创新的商业模式和先进的技术手段,积极推动保险行业实现数字化和智能化转型。保险科技的目标是提升保险服务的便利性、个性化和智能化水平,以前所未有的速度改变传统保险业的面貌。这一发展趋势为保险行业注入了新的活力,使保险产品更贴近人民群众的实际

致敬TempleOS,有开发者创建了启动Llama 2的操作系统,网友:8G内存老电脑就能跑致敬TempleOS,有开发者创建了启动Llama 2的操作系统,网友:8G内存老电脑就能跑Oct 07, 2023 pm 10:09 PM

不得不说,Llama2的「二创」项目越来越硬核、有趣了。自Meta发布开源大模型Llama2以来,围绕着该模型的「二创」项目便多了起来。此前7月,特斯拉前AI总监、重回OpenAI的AndrejKarpathy利用周末时间,做了一个关于Llama2的有趣项目llama2.c,让用户在PyTorch中训练一个babyLlama2模型,然后使用近500行纯C、无任何依赖性的文件进行推理。今天,在Karpathyllama2.c项目的基础上,又有开发者创建了一个启动Llama2的演示操作系统,以及一个

快手黑科技“子弹时间”赋能亚运转播,打造智慧观赛新体验快手黑科技“子弹时间”赋能亚运转播,打造智慧观赛新体验Oct 11, 2023 am 11:21 AM

杭州第19届亚运会不仅是国际顶级体育盛会,更是一场精彩绝伦的中国科技盛宴。本届亚运会中,快手StreamLake与杭州电信深度合作,联合打造智慧观赛新体验,在击剑赛事的转播中,全面应用了快手StreamLake六自由度技术,其中“子弹时间”也是首次应用于击剑项目国际顶级赛事。中国电信杭州分公司智能亚运专班组长芮杰表示,依托快手StreamLake自研的4K3D虚拟运镜视频技术和中国电信5G/全光网,通过赛场内部署的4K专业摄像机阵列实时采集的高清竞赛视频,

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Tools

EditPlus Chinese cracked version

EditPlus Chinese cracked version

Small size, syntax highlighting, does not support code prompt function

SublimeText3 English version

SublimeText3 English version

Recommended: Win version, supports code prompts!

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

SublimeText3 Linux new version

SublimeText3 Linux new version

SublimeText3 Linux latest version

SAP NetWeaver Server Adapter for Eclipse

SAP NetWeaver Server Adapter for Eclipse

Integrate Eclipse with SAP NetWeaver application server.