r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Seq

r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Seq

Apply the module to `x` given `emb` timestep embeddings.

Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb:

Apply QKV attention. :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor afte

Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor afte

Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.

Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.

Initialize a pretrained Real-ESRGAN upsampler. Args: model_name_or_path (str, optional): The Hugging Face repo ID or path to local

" # modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the g

" # modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the g

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

Convenience method that returns a flattened version of the page tree from `Client.get_pages()`. Returns: list[dict[str, T.Any]]:

Constructs an exponential noise schedule.

Constructs an exponential noise schedule.

Constructs the noise schedule of Karras et al. (2022).

Constructs the noise schedule of Karras et al. (2022).

Constructs a continuous VP noise schedule.