实验结果表明,我们的方法可以有效地融合潜空间的特征和文本向量的特征,并生成清晰和准确的图像。与传统的DiffusionModel相比,我们的方法在减少噪音和提升图像质量方面取得了显著的改进。 总之,利用CrossAttention将潜空间的特征与另一模态序列(如文本向量)的特征融合,并将其应用到DiffusionModel的逆向过程中,可以有效地减少...
The cross-attention module of the original Stable Diffusion model. Hypernetwork注入额外的神经网络来转换Key和Value,如下图: Hypernetwork injects additional neural networks to transform keys and values. 本文提出的Prompt2Prompt方法,是通过编辑提示的方式在预训练的扩散模型中进行图像编辑,包括局部编辑(替换一个...
最近在研究如何使用self-guidance来引导图像生成(通过设计energy function引导修改attention map的方法来编辑图像,Diffusion Self-Guidance for Controllable Image Generation),code应该不会release了,而且原文章是在Imagen上实现的。我想把它在stable diffusion上试一下。 先找一些有代码的similar work来做一下参考。Prompt-...
Moreover, we propose a dynamic cross-attention modelling strategy to extract hierarchical cell-to-tissue information from histology images. Lastly, we propose a co-expression-based gene-correlation graph network to model the co-expression relationship of multiple genes. Experiments show that our method...
Mask-guided cross-image attention for zero-shot in-silico histopathologic image generation with a diffusion model 来自 arXiv.org 喜欢 0 阅读量: 10 作者:D Winter,N Triltsch,M Rosati,A Shumilov,Z Kokaragac,Y Popov,T Padel,LS Monasor,R Hill,M Schick ...
In the first stage, this paper innovatively proposes a dynamic cross-fusion attention mechanism (DCFA) . This module facilitates the model to exchange information between different patches of the time series, thereby capturing the complex interactions between variables across time. In the second stage...
In this paper, we introduce the concept of themes for the first time and optimize and improve the cascade information prediction model. 2.3. Prediction of information diffusion based on attention mechanism Since the attention mechanism was proposed in 2014, it has gradually shown powerful capabilities...
《CASSPR: Cross Attention Single Scan Place Recognition》(ICCV 2023) GitHub: github.com/Yan-Xia/CASSPR《DECO: Dense Estimation of 3D Human-Scene Contact in the Wild》(ICCV 2023) GitHub: github.com/sha2nkt/deco [fig2] 《FashionTex: Controllable Virtual Try-on with Text and Texture》(...
PyTorch source code for "Stacked Cross Attention for Image-Text Matching" (ECCV 2018) computer-visiondeep-learningneural-networkpytorchimage-captioningcross-modalvisual-semantic UpdatedMay 18, 2023 Python Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audi...
加词,则是直接在对应位置加入新的attention map。 token增强——直接提高对应的map的权重。 都建立在已经用一个prompt输入的基础上,但是如果是只有一个图,怎么直接修改? 用captioning?) 生成的图片不仅和random seed有关,text embedding与pixel之间相互的关系也很重要。(diffusion model利用cross-attention来融合图文的...