而文章也是进一步在多个backbone model,以及多种规模的LLM下验证了其有效性,其中,13B的LLM性能会有一定程度的提升,说明参数规模越大的LLM具有更优的文本编码能力,这里不再具体展开,感兴趣的朋友可以参考原文。 SUR-Adapter使用不同参数规模的LLM在不同diffusion backbone model上的性能对比 MiniGPT-5 说实话这篇工作的...
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds Snap Research团队通过引入高效的网络架构和改进步骤蒸馏,实现了移动端推理时间不到2秒的文本到图像扩散模型,让移动端本地跑SD模型成为可能 NeurIPs 2023:SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seco...
https://lukashoel.github.io/ViewDiff/ 2、NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging 布局感知的文本到图像生成,是一种生成反映布局条件和文本条件的多物体图像的任务。当前的布局感知的文本到图像扩散模型仍然存在一些问题,包括文本与布局条件之间的不匹配以...
Prompt engineering is the process of designing and fine-tuning the input text prompts that are used to train and evaluate text-to-image models. The goal of prompt engineering is to create prompts that are both diverse and representative of the types of images that the model will be used to...
七、DAMSM (Deep Attentional Multimodal Similarity Model) 7.1、DAMSM框架 DAMSM主要有两个神经网络,文本编码器和图像编码器。其将句子的图像和单词的子区域映射到一个公共语义空间,从而在单词级别测量图像-文本相似度,以计算图像生成的细粒度损失。 文本编码器:采用双向长短期记忆网络(LSTM) ...
Source:https://lilianweng.github.io/posts/2021-07-11-diffusion-model... 而扩散模型的重要贡献之一就是:在训练的过程中(例如 DDPM 的训练过程),通过噪声估计模型 ϵθ(xt,t) 来预测真实噪声,以最小化估计噪声与真实噪声之间的差距。后面我们会详细阐述这一贡献。
You can use the model simply through the notebooks here. The Stage B notebook only for reconstruction and the Stage C notebook is for the text-conditional generation. You can also try the text-to-image generation on Google Colab. Using in 🧨 diffusers Würstchen is fully integrated into ...
On June 11, 2024, OpenAI announced a collaboration with Apple to deeply integrate the ChatGPT generative language model into Apple's product lineup. With support from various generative AI models, devices like smartphones will become more intelligent. The text-to-image diffusion...
Model overview We propose a text-to-image model based on a sentence–word fusion perceptual generation adversarial network. The architecture of our method is shown in Fig. 2, which has been divided into three main parts: text encoder, generator and discriminator. The text encoder firstly encodes...
Snap Research团队推出了SnapFusion模型，该模型将文本到图像扩散过程实现于移动设备上，仅需不到2秒的时间。这一创新成果在NeurIPs 2023会议上得到了展示。在文本到图像扩散模型的推理过程中，主要由三个关键模块组成：Text Encoder（ViT）、UNet以及VAE Decoder（VAE）。以SDv1.5在iPhone 14 Pro上的测试...