Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only de

'inception_v3', 'resnet50', 'densenet121', 'vgg16_bn'

Register the backward hook for the attention dropout (This code is copied from https://github.com/zhipeng-wei/PNA-PatchOut)

Copied from https://github.com/jpzhang1810/TGR/blob/master/methods.py

'inception_v3', 'resnet50', 'densenet121', 'vgg16_bn' depth: [1, 2, 3, 4]

Adjust adversarial example

CFM+everywhere

Adaptive gradient modulation :param ori_data: natural images :param cur_adv: adv examples in last iteration :param gr

Scale the model Scale the input model's parameters of convolutional layer in a random way. ### Args: model: Model to scale.

Hard Mixed-Frequency Inputs (HMFI): Simple hard frequency mixing - Keep low frequency from input x - Replace high frequency w

Soft Mixed-Frequency Inputs (SMFI): Soft frequency mixing with smooth transitions - Use soft mask for smooth frequency transitions

Create a 1D, 2D, or 3D average pooling module.

Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [

Optimized blended gradient calculation using batch processing

Optimized integrated gradient calculation using batch processing

Defaults for classifier models.

Restore original forward methods

Flush, close possible files, and remove stdout/stderr mirroring.

Compute the mean for the previous step, given a function cond_fn that computes the gradient of a conditional log probability with res

Compute what the p_mean_variance output would have been, should the model's score function be conditioned by cond_fn. See co

tensordot(x, y, 1).

Fused `tf.nn.conv2d()` followed by `downsample_2d()`. Padding is performed only once at the beginning, not between the operations. The fused

2-dimentional Discrete Cosine Transform (DCT)

2-dimentional Discrete Cosine Transform, Type II (a.k.a. the DCT) (This code is copied from https://github.com/yuyang-long/SSA/blob/m

2-dimentional Discrete Cosine Transform, Type II (a.k.a. the DCT) (This code is copied from https://github.com/yuyang-long/SSA/blob/m

2-dimentional Discrete Cosine Transform, Type II (a.k.a. the DCT) (This code is copied from https://github.com/yuyang-long/SSA/blob/m

========================================== ============ DDIM Inversion ============== ===========================

Sample x_{t-1} from the model using DDIM. Same usage as p_sample().

Use DDIM to sample from the model and yield intermediate samples from each timestep of DDIM. Same usage as p_sample_loop_pro

Forward hook for residual modules Inputs: lamb (float): the weight for the residual modules

Discretize the SDE in the form: x_{i+1} = x_i + f_i(x_i) + G_i z_i. Useful for reverse diffusion sampling and probabiliy flow sampling. Defau

Compute the log-likelihood of a Gaussian distribution discretizing to a given image. :param x: the target images. It is assumed that thi

disparity-reduced filter :param grads: gradients of each model :param data_size: size of input images :return: reduce

Get the drift function of the reverse-time SDE.

Enumerate and store references to attention and FFN modules Also stores original forward methods for later restoration