2.4 Classifier-free guidance 按照Conditional DDPM的采样过程的话,采样得到的结果虽然有很高的多样性,但是得到的结果可能并不能达到很好的真实性,而且结果和语义图之间的关系也可能得不到很好的保持。在之前的文章中提到过,conditional diffusion model采样的质量可以通过加入 $\triangledown_{y_t}log p(x | y_t)...
Our approach combines the semantic classification ability of the cross-attention module within the diffusion model with score-based conditional guidance to achieve high-quality image reconstruction and precise identification of discriminative regions. Experimental results have demonstrated that our anomaly ...
为了进一步提高语义图像合成中的生成质量和语义可解释性,我们引入了无分类器引导( classifier-free guidance)的采样策略,该策略被公认为采样过程中的无条件模型的核心。我们在三个基准(baseline)数据集上进行的大量实验,证明了提出的方法的有效性,在保真度 (FID) 和多样性 (LPIPS) 方面实现了目前最高水平的效果。 1...
which presents a substantial hurdle. Finding a series of noise vectors that, when given as an input to a diffusion process, would result in the input picture is necessary for this. The denoising diffusion implicit model (DDIM) technique,...
we introduce the classifier-free guidance sampling strategy, which acknowledge the scores of an unconditional model for sampling process. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed method, achieving state-of-the-art performance in terms of fidelity (FID...
We explore fine-grained, continuous control of this model class, and introduce a novel unified framework for semantic diffusion guidance, which allows either language or image guidance, or both. Guidance is injected into a pretrained unconditional diffusion model using the gradient of image-text or ...
SPGNet: Semantic Prediction Guidance for Scene Parsing [paper]Adaptive Context Network for Scene Parsing [paper]Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation NIPS 2019 [paper]Neural Diffusion Distance for Image Segmentation NIPS 2019 [paper]...
Text-to-image diffusion models are zero-shot classifiers. arXiv preprint arXiv:2303.15233, 2023. Couairon et al. [2023] Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. DiffEdit: Diffusion-based semantic image editing with mask guidance. In International Conference on ...
V是与U同分辨率的guidance feature map 系数D是g(XXX),梯度范数|∇V |2 可以作为可靠的语义边界指标来构建扩散系数。 实际上,靠近语义边界区域的扩散将被抑制,而远离语义边界的扩散会加速。 解方程:performing the finite difference update rule for T steps. ...
接着,for each training image, this autogressive Transformer teacher model predicts high-quality sentence Stea as additional semantic guidance. Then we feed the predicted high-quality sentence Stea into the cascaded Diffusion Transformer, instead of the ground-truth sentences. yaya: 不是很懂 将 Stea ...