sample的时候可以选择 clip_guided.ipynb 对应的classifier guide:先classifier free得到\mu_\theta(通过cross attention),然后再通过x_{t-1}\leftarrow ~\mathcal{N}(\mu_\theta+\color{red}{s\Sigma_\theta\nabla_{x_t}\log F_\phi(x_t,\text{guide})}, \Sigma_\theta)。也可以选择text2im.ipynb...
项目链接: DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps:https://arxiv.org/abs/2206.00927(NeurIPS 2022 Oral) DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models:https://arxiv.org/abs/2211.01095 项目开源代码:https://github...
设计fast ODE sampler,如DDIM,DPM solver(DDIM 是一阶的 DPM-Solver,DPM-Solver++ 负责将 DPM-Solver 扩展到 Guided Sampling 的情况)。 一文读懂扩散模型原理、推断加速和可控生成_鲟曦研习社www.kuxai.com/article/726 蒸馏加速(training-based) 蒸馏Diffusion的时间步(Distillation TimeSteps),实现更少的时间...
项目链接: DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps:https://arxiv.org/abs/2206.00927(NeurIPS 2022 Oral) DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilisti...
接着，来到2021年的二月，OpenAI发布了“Improved Diffusion”：这篇论文提出了后来被广泛采用的Cosine Noise Schedule，Importance sampling，以及Stride Sampling加速采样等技术。继之而来的，是2021年五月OpenAI所发布的“Classifier Guidance”（亦被称为Guided Diffusion）。这篇论文提出了一项重要的策略，即通过基于分类器...
Chen H, Chen J, et al. Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling ...
1、Accelerating Diffusion Sampling with Optimized Time Steps 2、DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models 3、Balancing Act: Distribution-Guided Debiasing in Diffusion Models 4、Few-shot Learner Parameterization by Diffusion Time-steps 5、Structure-Guided Adversarial Train...
{\phi}\left(y \mid x_{t}\right)\right|_{x_{t}=\mu}。因此，Classifier Guided Sampling ...
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models AI and ML Accelerator Survey and Trends Large-batch Optimization for Dense Visual Predictions ArXiv Weekly Radiostation：NLP、CV、ML 更多精选论文（附音频）论文 1：Closed-form Continuous-time Neural Networks 作者：Ramin ...
autoencoder是一个基于encoder-decoder架构的图像压缩模型,对于一个大小为的输入图像,encoder模块将其编码为一个大小为的latent,其中为下采样率(downsampling factor)。在训练autoencoder过程中,除了采用L1重建损失外,还增加了感知损失(perceptual loss,即LPIPS,具体见论文The ...