Hierarchical Text-Conditional Image Generation with CLIP Latents 是一种层级式的基于CLIP特征的根据文本生成图像模型。 层级式的意思是说在图像生成时,先生成64*64再生成256*256,最终生成令人叹为观止的1024*1024的高清大图。 DALLE·2模型根据CLIP的文本特征和图像特征最终生成图像,
A key advantage of using CLIP compared to other models for image representations is that it embeds images and text to the same latent space, thus allowing us to apply language-guided image manipulations (i.e., text diffs), which we show in Figure 5. 细节见原文~ 四、探测CLIP潜在空间 作者...
Text-to-Image Generation Results Limitations TL;DR 也就是著名的DALL·E 2,结合CLIP和Diffusion Model的text-to-image方法 project page Overview 总体上来说,下图很直观地表示了方法的主体,主要看下图虚线下方的部分(上方展示的是CLIP的训练方法),文字输入CLIP的text encoder得到embedding,该embedding首先被送到autor...
对于CLIP来说,它是给定文本和图像,然后得到特征,可以拿特征去做图像匹配、图像检索之类的工作,是一个从输入到特征的过程;对于DALL·E 2来说,它是通过文本特征,然后到图像特征,最后到图像的过程,其实就是CLIP的反过程,把特征又还原到数据,所以整个框架叫做unCLIP。 方法 训练数据集采用图像文本对,给定图像x,用zi表...
UnCLIP是通过transformer输入text-condition作为input,扩散出CLIP image embedding(一维)。 LDM把text-condition通过cross attention混合到latent diffusion的UNet中间层,扩散出latent feature(猜测是二维)。 DM Prior的学习目标不同: UnCLIP是以学习denoise之后的image embedding作为目标 ...
Text-to-image generation (T2I) has been a popular research field in recent years, and its goal is to generate corresponding photorealistic images through natural language text descriptions. Existing T2I models are mostly based on generative adversarial networks, but it is still very challenging to...
Unsupervised Attention-guided Image-to-Image Translation backgroundtext 这是NeurIPS 2018一篇图像翻译的文章。目前的无监督图像到图像的翻译技术很难在不改变背景或场景中多个对象交互方式的情况下将注意力集中在改变的对象上去。这篇文章的解决思路是使用注意力导向来进行图像翻译。下面是这篇文章的结果图: ...
在16位精度下,text-to-image任务预训练会非常不稳定,保持训练的稳定是CogView最具有挑战的部分。在分析模型训练后,发现有两种不稳定性:溢出(NAN loss)和下溢(loss 不收敛),因此提出以下维稳技术: 4.1 Precision Bottleneck Relaxation (PB-Relax) 在分析了训练的动态性之后,作者发现溢出(NAN loss)总是发生在两个瓶...
More- over, StyleCLIP [40] and StyleMC [34] both propose using CLIP for text-guided manipulation of both randomly gener- ated and encoded images with StyleGAN2. These methods show that it is possible to use CLIP for fine-grained and dis- entangled manipulations of ...
In Murdoch's architecture, the generator is a model called BigGAN, and CLIP is used to guide the generation process with text. This inspired Katherine Crowson to connect a more powerful neural network (VQGAN) with CLIP (Crowson et al., 2022). The VQGAN–CLIP architecture became very ...