this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch

#Run settings diffusion_steps = int(ddimSteps) #@param [25, 50, 100, 250, 500, 1000] eta = 1.0 #@param {type: 'raw'}

Generate a video from a sequence of prompts and seeds. Optionally, add audio to the video to interpolate to the intensity of the audio.

Zero out the parameters of a module and return it.

Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pas

Updates paths inside resnets to the new naming scheme (local renaming)

:param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])

Determines and returns the next sequence number to use when saving an image in the specified directory. If a prefix is given, only consi

Compute the intermediate time steps for sampling. Args: skip_type: A `str`. The type for the spacing of the time steps. We support

Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.

Removes segments. Positive values shave the first segments, negative shave the last segments.

Init. Args: scale_factor (float): scaling mode (str): interpolation mode

Imagenet Superresolution Dataloader Performs following ops in order: 1. crops a crop of size s from image either as random o

Calculates the noise level (sigma_down) to step down to and the amount of noise to add (sigma_up) when doing an ancestral sampling step.

grad converts h to the right gradient vector and return the dot product with (x,y)