对于模型能力上的升级,SUR-Adapter通过一个轻量级的Adapter网络将LLMs的表征与CLIP编码得到的表征形状对齐,其中,Adapter网络由一个简单的attention组成,而将LLMs表征进行蒸馏的过程则通过优化query的方式进行,通过计算query和LLM表征之间的KL散度,让CLIP编码出来的文本表征分布与LLM表征尽可能相近。其具体损失函数计算如下: ...
The central idea of generating images with diffusion model relies on the fact that we have powerful computer vision models. Given a large enough dataset, these models can learn complex operations. Diffusion models approach image generation by framing the problem as following: 利用扩散模型生成图像的核...
Latent Diffusion Models (LDM, CVPR-22) 首次提出在特征空间做扩散和逆扩散,扩散模型用attention模型实现。 显著提升了efficiency。 提出了用cross-attention加入condition信息,可以灵活的嵌入(caption, layout, image, mask)等condition。 Paper List (DDPM) Denoising Diffusion Probabilistic Models. NIPS 20. (Diffusio...
Faster sampling (i.e. even lower values of ddim_steps) while retaining good quality can be achieved by using --ddim_eta 0.0 and --plms (see Pseudo Numerical Methods for Diffusion Models on Manifolds).Beyond 256²For certain inputs, simply running the model in a convolutional fashion on ...
In this regard, compartmental epidemiological models have been a main focus of attention. Among them, SIR models stand out, which are based on the assumption that the population can be classified into three independent compartmentalized groups (susceptible, infected, and recovered). The number and ...
Img2img Alternative, reverse Euler method of cross attention control Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions Reloading checkpoints on the fly Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one ...
Recently, diffusion models28have garnered a huge amount of attention in computer vision tasks29,30,31, especially in point cloud generation32,33,34which shares similarities with 3D molecule generation. These methods excel at inpainting 3D objects by learning the joint distribution. Although there is...
text condition将通过CrossAttention模块嵌入进来，此时Attention的query是UNet的中间特征，而key和value则是...
In order to leverage the memory efficient attention to speed up the unet we only need to update the file indiffusers/src/diffusers/models/attention.pyand add the following two blocks import xformers import xformers.ops from typing import Any, Optional ...
可以将下载的Stable Diffusion模型放在目录stable-diffusion-webui/models/Stable-diffusion/ 下。 例如,假设我们要做inpaint的调整。先在huggingface下载stable-diffusion-inpainting的checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting 并存放在stable-diffusion-webui/models/Stable-diffusion/ 下。