3.Decoding and Controllable Generation with Diffusion-LM 考虑完diffusion在文本应用中的修改之后,这一节开始介绍怎么将可控生成加入到文本生成中。 3.1Controllable Text Generation Diffusion-LM直接在连续隐变量中添加条件,即 p(x0:T|c)=∏t=1Tp(xt−1∣xt,c)。 根据贝叶斯定理: p(xt−1∣xt,c)∝p(...
作者们基于该模型,将其推广到了 controllable text generation,即我们并不只是单纯生成文本,而是基于一些control (这些 control 可以是结构上的,也可以是语意上的) 来生成满足相应要求的文本,只需要整个模型都 conditioned on control,包含 diffusion models 也将变为 conditional diffusion models。最终,作者们通过6种 ...
1.Stable Diffusion 的组件概览 来自Stability AI 的 Stable Diffusion 是一个由多个组件和模型组成的系统。它不是一个单一的模型。其主要组件如下图所示: Source:https://jalammar.github.io/illustrated-stable-diffusion/?trk=... 我们从高级视图开始,我们将在本文后面详细介绍更多细节。首先,这个文本编码器其实是...
Source:https://jalammar.github.io/illustrated-stable-diffusion/?trk=cndc-detail 图像生成器经历了两个阶段。 第一阶段:图像信息创建者(Image Information Creator) 这种组件是 Stable Diffusion 的秘诀。与以前的模型相比,它在性能上有很大提升。此组件运行多个步骤以生成图像信息,这是 Stable Diffusion 接口和库中...
3.2. Conditional Diffusion Model Different from natural image generation in vanilla dif- fusion models [15, 23, 48], the proposed CTIG-DM intro- duces expert knowledge related to text image, i.e., image condition, text condition, and style condition. As shown in Fig. 3, fo...
Recent diffusion-based generative models show promise in their ability to generate text images, but limitations in specifying the styles of the generated texts render them insufficient in the realm of typographic design. This paper proposes a typographic text generation system to add and modify text ...
Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently. However, natural language exhibits a far more pron...
Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise Zheng-Wen Lin, Yeyun Gong, Yelong Shen, Tong Wu, Zhihao Fan, Chen Lin, Nan Duan, Weizhu Chen 2022 GENIE : Large Scale Pre-training for Generation with D...
[LG] Text-Guided Molecule Generation with Diffusion Language Model O网页链接 提出一种新方法TGM-DLM,用于文本引导的分子生成。与现有基于SMILES字符串的自回归方法不同,TGM-DLM采用扩散模型,同时迭代更新SMILES token嵌入,分为两阶段:首先根据文本描述从随机噪声中优化嵌入,然后纠正无效的SMILES字符串。研究表明TGM-...
GLIDE(Guided Language to Image Diffusion for Generation and Editing) 时间:22/03 机构:OpenAI TL;DR 本文研究使用Diffusion Model做图像生成过程,如何更好地加入conditional信息。主要尝试两种方法: CLIP-guidance, Classifier-free guidance,并且证明了后者效果更佳。文本是后续DALLE2的重要baseline。