Attempts to acquire a job token, optionally blocking until available

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

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

this function tests if prompt is too long, and if so, adds a message to comments

this function tests if prompt is too long, and if so, adds a message to comments

Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient.

A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.pr

A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.pr

Return the data prediction model (with thresholding).

:param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and

Helper to embed some text

Helper to embed some text

Helper to embed some text

6t^5 - 15t^4 + 10t^3

Explanation: Getting good results in/out-painting with stable diffusion can be challenging. Although there are simpler effective solution

Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuou

Return a human readable size from bytes.

Helper to initialize noise

Helper to initialize noise

Helper to initialize noise

A piecewise linear function y = f(x), using xp and yp as keypoints. We implement f(x) in a differentiable way (i.e. applicable for autograd).