The Multimodal Generator works by using the CLIP's enconding capabilities to bring the text inputs and generated images from VQGAN to a same latent space, which allow to calculate a loss and then optimize the VQGAN output. The model was built using Google Colab to leverage it GPU's avail...
Zero-Shot Text-to-Image Generation, ICML 2021. [2] Esser et al. Taming Transformers for High-Resolution Image Synthesis, CVPR 2021. [3] Karras et al. A Style-Based Generator Architecture for Generative Adversarial Networks, CVPR 2019. [4] Sharma et al. Conceptual Captions: A Cleaned, ...
文本到图像模型(Text-to-image model)是一种机器学习模型,它将自然语言描述作为输入并生成与该描述匹配的图像。由于深度神经网络的进步,此类模型在 2010 年代中期开始开发。2022 年,最先进的文本到图像模型的输出,例如 OpenAI 的DALL-E 2、Google Brain 的Imagen和 StabilityAI 的Stable Diffusion开始接近真实照片和手...
可以控制图片局部生成,一个word-level的generator。有github代码:https://github.com/mrlibw/ControlGAN。 4.CPGAN Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis content parsing。同时parse文本和图片。设计了一个memory structure。使用了一个conditional discriminator来判断...
[38] conditioned their generator and discriminator on caption encodings where their StackGAN model generates images in multiple stages; stage-I generates a coarse low-resolution (64 × 64) image, and stage-II generates the final high-resolution (256 × 256) image. This stacking of ...
ControlGAN。可以控制图片局部生成,一个word-level的generator。有github代码:https://github.com/mrlibw/ControlGAN。 4.CPGAN Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis content parsing。同时parse文本和图片。设计了一个memory structure。使用了一个conditional discrimi...
DreamFusion采用的DM模型出自谷歌的Imagen[1],Imagen通过DM可以实现text-to-image的效果。DreamFusion将Imagen作为文生图的“工具”,希望在给定的文本text作为输入的情况下,得到文本对应的图像。在图像的生成过程中,不同视角的生成受到文本中与方向有关的描述所控制。 这里为文章埋下两处不足:1、Imagen的输出分辨率是...
项目地址：https://dreamfusion3d.github.io/ DreamFusion 可以借助预训练 2D text-to-image diffusion model，实现 text-to-3D synthesis。DreamFusion 引入了一个基于概率分布蒸馏 (probability density distillation) 的 loss，使 2D diffusion model 能够作为参数图像生成器 (parametric image generator) 优化的 ...
To achieve this, we introduce a word-level spatial and channel-wise attention-driven generator that can disentangle different visual attributes, and allow the model to focus on generating and manipulating subregions corresponding to the most relevant words. Also, a word-level discriminator is proposed...
It won't work, using the Tauri API gives us ability to do things that would not be allowed on your regular browser. I am not sure if the third-party service is using DALL-E or any other model and idc really ¯\(ツ)/¯