基本思路就是,在反向过程的每一步中,先通过任意的 SDE 求解器(Predictor)选择一个合适的步长 \Delta t<0,并预测出来该步长下的下个采样结果;然后通过任意一种仅依赖 score function 的 MCMC 过程(Corrector,如 Langevin dynamics,Hamiltonian Monte Carlo),基于 score-based
这篇文章[1]采用了 conditional diffusion model 来做时间序列的 imputation 以及 forecasting 任务。本文的亮点在于,diffusion model 的网络结构不再是 CSDI[2] 中的transformer 结构,而是 structured state-space model(SSM)。我们可以把这种结构理解为 RNN、一维 CNN 以及transformer 的平替结构,都是 seq-to-seq 模...
Diffusion Model-Based Image Editing: A Survey (TPAMI 2025) - SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods
git clone --depth 1 git@github.com:LeCAR-Lab/model-based-diffusion.git pip install -e. Usage To run model-based diffusion to optimize a trajectory, run the following command: cdmbd/planners python mbd_planner.py --env_name$ENV_NAME ...
The first model is based on the linear threshold propagation model. In addition, by considering multi-step information propagation in one time period, this paper proposes a learning model for multi-step diffusion influence between pairs of users based on the idea of random walk. Mixed integer ...
cross-attention layers are utilized to calculate the attention scores of the molecule and protein, protein pocket. Additionally, a dual diffusion strategy is employed to enable the model to discern atom-wise forces. This strategy involves constructing two types of virtual edges. Firstly, pairs of ...
To reduce the number of LAMMPS simulations, a screening step of the MOFs’ adsorption performance is conducted on the 18,770 MOFs described above using a modified version of the CGCNN model that we introduced in Ref.37. Here, we used MOF structures from the hMOF dataset as well as their ...
Fig. 1. Schematics of current standard model of brain microstructure (a) and the novel model proposed in this work (b). Current conjecture envisions the tissue component in an MRI voxel as subdivided into two non-exchanging compartments: intra-neurite and extra-neurite space. The total MRI si...
为了讲述其算法,首先规定一些符号锚定实体的语义信息锚定空间位置信息e:锚定实体的语义信息l:锚定空间位置信息这里的锚定实体就是要控制位置的对象。e通常是一段文本(如 prompt),l通常是边界框来表示位置信息。作者将控制 grounded text-to-image model 的指令建模为: ...
export MODEL_FLAGS_128="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --image_size 128 --learn_sigma True --noise_schedule linear --num_channels 256 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 False --use_scale_shift_norm True...