另外注意到,应用Classifier-Free Guidance的diffusion极大地提高了生成样本质量,故LDM-KL-8-G与以往最先进的AR和diffusion模型在文本到图像合成方面的性能不相上下,同时大大减少了参数量。 另外还在OpenImages上训练了基于语义布局(semantic layouts)的图像合成模型,并在COCO上进行了微调,结果如上图。 另外评估了在Image...
We introduce SQ-DiffuPep, a novel latent diffusion-based model that significantly outperforms existing methods in discovering highly active antimicrobial peptide candidate sequences.Our innovative soft quantization and shifted-window attention mechanisms effectively address the codebook collapse problem in VQ-...
Stable Diffusion 的采样过程可以被当作一个定义在二维 Lattice 上标量场的准平衡态动力学演化。 标量场 很显然,Stable Diffusion 的采样的目标是一个离散化的二维格点,或者说位图。只要有常识且不是盲人,应该没有任何理解困难。 其实这一点其实是最晦涩的,因为几乎所有的理论形式化都不涉及空间概念。甚至在我最初的...
(2024). DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15012. Springer, Cham. https://doi...
Our 1.45B latent diffusion LAION model was integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo: More pre-trained LDMs are available: A 1.45B model trained on the LAION-400M database. A class-conditional model on ImageNet, achieving a FID of 3.6 when using classifi...
Fig. 1. Illustration of our conditional reservoir generation problem in which the generative model stochastically samples a realistic reservoir (right) given the limited measurements (left). The regions with no information are denoted in gray. Motivated by the progress made with diffusion models on ...
Exploiting Latent Information to Predict Diffusions of Novel Topics on Social Networks - tsungtingkuo/diffusion
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models Andreas Blattmann1 *,† Robin Rombach1 *,† Huan Ling2,3,4 * Tim Dockhorn2,3,5 *,† Seung Wook Kim2,3,4 Sanja Fidler2,3,4 Karsten Kreis2 1LMU Munich 2N...
这篇论文《High-Resolution Image Synthesis with Latent Diffusion Models》提出了一种在计算资源受限条件下进行高分辨率图像合成的新方法,通过在潜在空间中应用扩散模型,并引入交叉注意力机制,成功实现了复杂度降低和细节保留的最佳平衡。 This passage provides an overview of the recent advancements in image synthesis...
To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-...