Multi-modal Latent Diffusion 7 Jun 2023 · Mustapha Bounoua, Giulio Franzese, Pietro Michiardi · Edit social preview Multi-modal data-sets are ubiquitous in modern applications, and multi-modal Variational Autoencoders are a popular family of models that aim to learn a joint representation of ...
具体来说,DiT遵循Latent Diffusion Model (LDM)框架的基础,并通过引入全面的DiT设计空间,包括块大小、变压器块架构和模型大小来模拟Vision Transformer (ViT)的设计。DiT的第一层称为patchify,将空间输入线性嵌入到一个序列的token中。在patchify步骤之后,输入token会经过一系列包含时间条件和标签条件的transformer块进行...
To address these issues, we introduce a novel multi-modal latent diffusion model (MM-LDM) for the SVG task. We first unify the representation of audio and video data by converting them into a single or a couple of images. Then, we introduce a hierarchical multi-modal autoencoder that ...
视频地址: 【北大微软 可控图像生成最新工作】Unified Multi-Modal Latent Diffusion for Joint Subject and Text ws1803 粉丝:218文章:5 关注整体的情况大概如上图,整体式在Stable Diffusion框架下进行的。相当于一些预处理工作分享到: 投诉或建议 评论1 最热 最新 请先登录后发表评论 (・ω・) 发布 旺旺2023...
Latent Guard [449], SafetyBench [468], GOAT-Bench [469] 扩散模型: Denoised Diffusion Probabilistic Models [128] 变换器(Transformers): GPT [138]–[140], LLaMA [137] 生成式AI: Stable Diffusion (SD) [10], VideoDiffusion [14], AudioLDM [44] 多模态理解和生成: CLIP [23], CLAP [131]...
In this work, we present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training. Our key insight is that diffusion models driven by different modalities are inherently complementary regarding the latent ...
using RFdiffusion, categorized under EC2, EC3, EC4, and EC5. Due to the multifunctional nature of the EC1 class enzymes used in the original study, we excluded this category from our analysis. BLASTp requires alignment with similar enzyme structures to predict active sites, while AEGAN relies ...
High-resolution image syn- thesis with latent diffusion models. In CVPR, pages 10674– 10685. IEEE, 2022. 3 [29] Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, and Chaowei Xiao. Test- time prompt tuning for zero-shot gener...
基于最近的潜在扩散模型(Latent Diffusion Models, LDMs),GenAD能够处理驾驶场景中具有挑战性的动态变化。 引入新颖的时间推理模块,包括因果时序注意力和解耦空间注意力,以有效模拟驾驶场景中的剧烈时空变化。 通过两阶段学习策略,首先在图像域上微调预训练的LDM,使其适应驾驶领域,然后在视频预测预训练中引入时间推理模块...
图像:通过 vae 编码为 latents ,由连续向量表示,在图像前后添加特殊的 BOI 和 EOI 标记符。 模型训练 这个图 1 我理解是训练过程,在推理阶段 Diffusion 部分差异还是挺大的图像部分容易产生歧义,transformer 并不能直接出图,只是预测 noise,出图需要 denoise 250 个 step,而且还需要linear/unet + vae.decoder。