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.

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

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

:param H: :param W: :param N_rays: :param images: [B,3,H,W] :param intrinsics: [B, 3, 3] :param c2ws: [B, 4, 4] :param d

generate random rays in world space, for a single image sample rays from local patches :param H: :param W: :param N_rays: the num

Args: tar_pose: target pose [N, 4, 4] ref_poses: reference poses [M, 4, 4] num_select: the number of nearest views to sel

Get ray directions for all pixels in camera coordinate. Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/ r

return the gradient of specific lod :param x: :param lod: :return:

return the gradient of specific lod :param x: :param lod: :return:

Make huggingface dataset with appropriate list of transforms applied