一篇利用diffusion模型——LatentDiff,在隐空间,生成蛋白多构象的文章。该文做的是粗粒度生成,生成的是 Cα 原子的坐标。 文章链接:A Latent Diffusion Model for Protein Structure Generation 1. Abstract 蛋白质是复杂的生物分子,并且在生物体内发挥重要的功能。设计和生成新颖的蛋白质能够为未来许多合成生物学应用铺...
Inspired by the recent success of the Latent Diffusion Model (LDM), we propose ReF-LDM, an adaptation of LDM designed to generate HQ face images conditioned on one LQ image and multiple HQ reference images. Our model integrates an effective and efficient mechanism, CacheKV, to leverage the ...
因此，李飞飞学生团队与谷歌研发者共同提出了窗口注意力潜在Transformer（Window Attention Latent Transformer，W.A.L.T），这是一种基于Transformer的潜在视频扩散模型（latent video diffusion models，LVDM）方法。当下，市面上的同类工具如Pika Labs推出的Pika 1.0、Runway的Gen-2，大都采用扩散模型（Diffusion Model...
2. 潜在扩散模型(Latent Diffusion Model, LDM):与LSGM不同,潜在扩散模型(LDM)分别处理自编码器和扩散模型的训练。首先,训练一个自编码器以生成低维的潜在空间。然后,训练扩散模型以生成潜在代码。DALLE-2 采用了类似的策略,通过在CLIP图像嵌入空间上训练一个扩散模型,然后训练一个单独的解码器以基于CLIP图像嵌入创...
[Early Accepted at MICCAI 2023] Pytorch Code of "InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model" - BioMedAI-UCSC/InverseSR
Stable Diffusion is a latent text-to-image diffusion model. Thanks to a generous compute donation from Stability AI and support from LAION, we were able to train a Latent Diffusion Model on 512x512 images from a subset of the LAION-5B database. Similar to Google's Imagen, this model us...
For example, the techniques may include inputting latent variable data into a machine learned model. The machine learned model may output an object trajectory (e.g., position data, velocity data, acceleration data, etc.) for one or more objects in the environment based on the latent variable...
Unlike traditional generative models that often face training challenges, diffusion models use diffusion-based training techniques that enhance stability, resulting in more reliable model training. Interpretable Latent Space Some diffusion models offer interpretable latent spaces, providing valuable insights into...
High-resolution image synthesis with latent diffusion models. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 10684–10695 (IEEE, 2022). Lu, C. et al. DPM-Solver: a fast ODE solver for diffusion probabilistic model sampling in around 10 steps. Adv. Neural Inf. ...
Mainstream solutions to sequential recommendation represent items with fixed vectors. These vectors have limited capability in capturing items’ latent aspects and users’ diverse preferences. As a new generative paradigm,diffusion modelshave achieved excellent performance in areas like computer vision and na...