作者指出,由于Diffusion Transformers具有高效的计算性能,因此LDMs是探索新型架构的理想起点。最后,作者还说明了如何将Diffusion Transformers应用于潜在空间,并指出这种方法的灵活性和有效性, 即, 使用了基于混合的image generation pipeline方法,使用现成的卷积VAE 和基于Transformer的 DDPM。 3.2. Diffusion Transformer Design...
名称 DiT: Scalable Diffusion Models with Transformers 时间:23/03 机构:UC Berkeley && NYU TL;DR 提出首个基于Transformer的Diffusion Model,效果打败SD,并且DiT在图像生成任务上随着Flops增加效果会降低,比较符合scaling law。后续sora的DM也使用该网络架构。 Method 网络结构整体参考LDM,只不过将latent diffusion中...
DiT(Diffusuion Transformer)将扩散模型的 UNet backbone 换成 Transformer,并且发现通过增加 Transformer 的深度/宽度或增加输入令牌数量,具有较高 Gflops 的 DiT 始终具有较低的 FID(~2.27),这样说明 DiT 是可扩展的(Scalable),网络复杂度(以 Gflops 度量)与样本质量(以 FID 度量)之间存在强相关性。 架构 DiT ...
OpenAI发布Sora,以及Stability.AI发布的SD3,根据其技术报告,使用了可扩展的transformer扩展模型,《Scalable Diffusion Models with Transformers》是其相关的一篇重要论文。关于DIT作者进阶的论文SIT《SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers 》介绍,下一篇文章解析!
Scalable Diffusion Models with Transformers William Peebles* UC Berkeley Saining Xie New York University Figure 1: Diffusion models with transformer backbones achieve state-of-the-art image quality. We show selected sam- ples from two of our class-conditional DiT-XL/2 models trained on ImageNet...
【Sora平替】Scalable Diffusion Models with Transformers-人工智能/AI/Sora/视频模型, 视频播放量 457、弹幕量 0、点赞数 3、投硬币枚数 2、收藏人数 3、转发人数 1, 视频作者 靓仔学AI, 作者简介 一名已毕业老研究生,目前从事AI领域,不定期在B站上进行分享,需要资料的
We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of for...
Add Datasetsintroduced or used in this paper Results from the Paper Edit Ranked #16 onImage Generation on ImageNet 256x256 Get a GitHub badge TaskDatasetModelMetric NameMetric ValueGlobal RankResultBenchmark Image GenerationImageNet 512x512DiT-XL/2FID3.04# 20 ...
混元文生图大模型(下称:混元DiT,Scalable Diffusion Models with Transformers)由腾讯开源,包含模型权重、推理代码、模型算法等完整模型,
We train latent diffusion models, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops...