Autoregressive Model的目的是将原始的caption条件,离散化成更加丰富的条件。在训练阶段是和Diffusion一起优化的。Text Encoder的输出以cross attention的形式作用在Autoregressive Model上,迭代预测下一个token。最终训练好Autoregressive Model后,将Autoregressive Model最后一层的embedding和Text Encoder的concat在一起作为条件输...
本文来自于NIPS 2021[1],是diffusion model在时间序列领域基石性的文章。虽然本文重点关注的是时间序列的 imputation 任务,但是该方法也可以用于interpolation和forecasting任务。本文使用了conditional score-based diffusion model,以可以观测到的值为条件,来得到空缺值的条件分布,模型示意图如下所示: 使用CSDI 做时间序列 ...
a conditional latent diffusion model(LDM)(条件潜在扩散模型):它依赖于噪声mel嵌入xt,文本嵌入ctext和控件嵌入ccontrol在内的条件。(U-net冻结模块)[潜在表示捕捉了数据的主要特征,并且通常具有更简单的分布。] a variational auto-encoder(变分自编码器):由编码器和解码器组成,编码器和解码器将mel频谱图压缩到mel...
Samples generated from the model. The conditioning roughly follows the method described in Classifier-Free Diffusion Guidance (also used in ImageGen). The model infuses timestep embeddings t e and context embeddings c e with the U-Net activations at a certain layer a L , via, a L + 1 ...
To ensure robust, high-capacity, and secure communication, we propose a conditional diffusion model for coverless image steganography, called CDIS, which not only generates realistic stego images but also successfully extracts valid secret images in the case of distorted stego images. CDIS utilizes ...
PyTorch implementation of "Conditional diffusion model with spatial attention and latent embedding" [MICCAI 2024] Behzad Hejrati · Soumyanil Banerjee · Carri Glide-Hurst · Ming Dong Diffusion models have been used extensively for high quality image and video ge...
We train a generative diffusion model in a supervised way purely on observational data. We map observational and ESM data to a shared embedding space, where both are unbiased towards each other and train a conditional diffusion model to reverse the mapping. Our method can be used to correct ...
class conditional generation diffusion model (原创版) 1.条件生成扩散模型的概述 2.条件生成扩散模型的关键组成部分 3.条件生成扩散模型的应用实例 4.条件生成扩散模型的优势与局限性 正文 一、条件生成扩散模型的概述 条件生成扩散模型(Conditional Generative Diffusion Model)是一种基于深度学习的自然语言处理技术。
他们做过iGPT,而且最近刚写过 improved DDPM 这篇论文,也就是 denosing diffusion model,所以把扩散模型玩儿的是很溜。所以一会儿我们也可以看到 Dolly two 这个模型,其实就是 CLIP 模型加上 Glide 模型,而 Glide 模型就是一个基于扩散模型的文本图像生成的方法。那从作者这里也可以看出来,就是 CLIP 的作者加上...
Zhao, C., Dong, C., Cai, W.: Learning a physical-aware diffusion model based on transformer for underwater image enhancement. Preprint arXiv:2403.01497 (2024) Jiang, H., Luo, A., Fan, H., Han, S., Liu, S.: Low-light image enhancement with wavelet-based diffusion models. ACM Tran...