(DALL-E)Zero-Shot Text-to-Image Generation 引用: Ramesh A, Pavlov M, Goh G, et al. Zero-shot text-to-image generation[C]//International conference on machine learning. Pmlr, 2021: 8821-8831. 论文…
仅使用图像模态信息,训练一个dVAE,latent特征即visual codebook。好处:将256x256图像特征降维至32x32的image tokens(每个token的embedding dim为8192),提升了低频语义信息占比,降低了计算量。 Stage2: Learning the Prior 第一阶段dVAE模型是fixed,image tokens与text token concat之后输入Transformer。 Q: prior modul...
本文也就是DALL·E,用3.3 million image-text pairs训练了一个12B参数的autoregressive transformer,实现了高质量可控的text to image,同时也有zero-shot的能力 project page Method 自回归式的模型处理图片的时候,如果直接把像素拉成序列,当成image token来处理,如果图片分辨率过高,一方面会占用过多的内存,另一方面Likel...
Zero-Shot Text-to-Image Generation A. Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, I. Sutskever 2021 CogView: Mastering Text-to-Image Generation via Transformers Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang...
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a ...
Image credit: GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsBenchmarks Add a Result These leaderboards are used to track progress in Zero-Shot Text-to-Image Generation No evaluation results yet. Help compare methods by submitting evaluation metrics. ...
Afterwards, we trained a DALL-E model which accepts the VAE and our text data to learn to generate an image from the input text. Our DALL-E model aims to reduce the cross entropy loss weighted between text and image. Our tech stack uses PyTorch, the dalle-pytorch package, Weights & ...
Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic - YoadTew/zero-shot-image-to-text
PDF 引用 收藏 共1个版本 摘要原文 Converting a model's internals to text can yield human-understandable insights about the model. Inspired by the recent success of training-free approaches for image captioning, we propose ZS-A2T, a zero-shot framework that translates the transformer attention ...
pipeline(管道)是huggingface transformers库中一种极简方式使用大模型推理的抽象,将所有大模型分为音频(Audio)、计算机视觉(Computer vision)、自然语言处理(NLP)、多模态(Multimodal)等4大类,28小类任务(tasks)。共计覆盖32万个模型 今天介绍NLP自然语言处理的第十篇:零样本文本分类(zero-shot-classification),在huggin...