1、DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation 大型文本生成图像模型已取得显著进展,有能力从给定的文本提示中生成高质量和多样化的图像。然而,给定目标个体的一些参考图片(这里不妨称之为“主题”),这些模型还无法做到的是,在不同的上下文环境里去生成关于它们不同图片的能...
以及同期的一篇工作:An image is worth one word: Personalizing text-to- image generation using textual inversion,该方法提出了一种通过在冻结的文本到图像模型的嵌入空间中使用新的标记来表示视觉概念,如对象或风格,从而得到小型的个性化标记嵌入。然而这种方法受限于冻结扩散模型的表达能力。 三、方法 3.1 Designing...
另外,T5-XXL作为text-to-text generation的统一解决方案,其成功的原因也似乎与in-context learning有着异曲同工之妙。其在text-to-image diffusion models上的应用,证明了text-to-image diffusion models中,文本编码的能力并不一定需要CLIP中所携带的image-text alignment,即纯language models也可以用于编码文本信息。
12、Discriminative Probing and Tuning for Text-to-Image Generation 尽管在文本-图像生成(text-to-image generation)方面取得了进步,但之前方法经常面临文本-图像不对齐问题,如生成图像中的关系混淆。现有解决方案包括交叉注意操作,以更好地理解组合或集成大型语言模型,以改进布局规划。然而,T2I模型的固有对齐能力仍然...
More Control for Free! Image Synthesis with Semantic Diffusion Guidance Classifier-Free Diffusion Guidance Zero-Shot Text-to-Image Generation On Fast Sampling of Diffusion Probabilistic Models Vector Quantized Diffusion Model for Text-to-Image Synthesis...
By offering a streamlined process for both training and sampling, it presents a significant leap in performance over GANs, specifically in the realm of text-prompts. The study underscores the versatility of Diffusion Models to revolutionize various image-related domains.Ghadekar, Premanand...
1、ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models 3D资产生成正受到大量关注,受到最近文本引导的2D内容创建成功的启发,现有的文本到3D方法使用预训练文本到图像扩散模型来解决优化问题,或在合成数据上进行微调,这往往会导致没有背景的非真实感3D物体。
Text-to-image generation is a comprehensive task that combines the fields of Computer Vision (CV) and Natural Language Processing (NLP). Research on the methods of text to image based on Generative Adversarial Networks (GANs) continues to grow in popular
Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes...
Ruiz N., Li Y., Jampani V., Pritch Y., Rubinstein M. and Aberman K. DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation. arXiv preprint arXiv:2208.12242, 2022. 概 可控文生图. Motivation 之前的文生图模型缺乏可控性. 虽然我们可以通过特别的模型生成大差不差...